!pip install librosa
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: librosa in /usr/local/lib/python3.7/dist-packages (0.8.1) Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.21.6) Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.0.2) Requirement already satisfied: soundfile>=0.10.2 in /usr/local/lib/python3.7/dist-packages (from librosa) (0.11.0) Requirement already satisfied: numba>=0.43.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (0.56.2) Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (3.0.0) Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from librosa) (0.4.2) Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (21.3) Requirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.2.0) Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.6.0) Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (4.4.2) Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.7.3) Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa) (5.0.0) Requirement already satisfied: setuptools<60 in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa) (57.4.0) Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa) (0.39.1) Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->librosa) (3.0.9) Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa) (2.23.0) Requirement already satisfied: appdirs>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa) (1.4.4) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2022.9.24) Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (3.0.4) Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (1.24.3) Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa) (2.10) Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa) (3.1.0) Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.7/dist-packages (from soundfile>=0.10.2->librosa) (1.15.1) Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0->soundfile>=0.10.2->librosa) (2.21) Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.43.0->librosa) (3.9.0) Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.43.0->librosa) (4.1.1)
import torch
import torchvision
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import numpy as np
import torch.nn.functional as F
from torch.optim.lr_scheduler import StepLR
import librosa
# Get cpu or gpu device for training.
device = "cuda:0" if torch.cuda.is_available() else "cpu"
print(f"Using {device} device")
Using cuda:0 device
class NeuralNetwork(nn.Module):
def __init__(self):
super(NeuralNetwork, self).__init__()
# define the layers
self.flatten = nn.Flatten()
self.fc1 = nn.Linear(513, 512)
self.fc2 = nn.Linear(512, 512)
self.fc3 = nn.Linear(512, 512)
self.out = nn.Linear(512, 513)
def forward(self, x):
x = self.flatten(x)
out1 = F.tanh(self.fc1(x))
out2 = F.tanh(self.fc2(out1))
out3 = F.tanh(self.fc3(out2))
out = F.relu(self.out(out3))
# return output of each hidden layer
return out
model = NeuralNetwork().to(device)
print(model)
NeuralNetwork( (flatten): Flatten(start_dim=1, end_dim=-1) (fc1): Linear(in_features=513, out_features=512, bias=True) (fc2): Linear(in_features=512, out_features=512, bias=True) (fc3): Linear(in_features=512, out_features=512, bias=True) (out): Linear(in_features=512, out_features=513, bias=True) )
def xavier_initializer(layer):
if type(layer) == nn.Linear:
nn.init.xavier_normal(layer.weight)
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
model.train()
model.to(device)
for batch, (X, y) in enumerate(dataloader):
X, y = X.to(device), y.to(device)
# Compute prediction error
pred = model(X)
loss = loss_fn(pred, y)
# Backpropagation
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch % 10 == 0:
loss, current = loss.item(), batch * len(X)
print(f"Train loss: {loss:>7f}")
def train_epochs(train_data, model, loss_fn, optimizer):
epochs, accuracy = 1, 1
for i in range(500):
print(f"Epoch {epochs}\n-------------------------------")
train(train_data, model, loss_fn, optimizer)
epochs += 1
print("Done!")
s, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/train_clean_male.wav', sr=None)
S=librosa.stft(s, n_fft=1024, hop_length=512)
sn, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/train_dirty_male.wav', sr=None)
X=librosa.stft(sn, n_fft=1024, hop_length=512)
First, Train with the whole dataset to check the value, it should be really good because the model will overfit
batch_size = 2000
X = X.T
S = S.T
s_mag = np.abs(S)
x_mag = np.abs(X)
X = torch.from_numpy(x_mag)
S = torch.from_numpy(s_mag)
train_dataset = TensorDataset(X, S)
train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
#all the data
len(train_dataloader.dataset)
2459
model = NeuralNetwork().to(device)
loss_fn = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
model.apply(xavier_initializer)
scheduler = StepLR(optimizer, step_size=50, gamma=0.1)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_. This is separate from the ipykernel package so we can avoid doing imports until
train_epochs(train_dataloader, model, loss_fn, optimizer)
Epoch 1 -------------------------------
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1949: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
Train loss: 0.104239 Epoch 2 ------------------------------- Train loss: 0.065418 Epoch 3 ------------------------------- Train loss: 0.051938 Epoch 4 ------------------------------- Train loss: 0.043915 Epoch 5 ------------------------------- Train loss: 0.040460 Epoch 6 ------------------------------- Train loss: 0.031809 Epoch 7 ------------------------------- Train loss: 0.030910 Epoch 8 ------------------------------- Train loss: 0.028759 Epoch 9 ------------------------------- Train loss: 0.026416 Epoch 10 ------------------------------- Train loss: 0.025882 Epoch 11 ------------------------------- Train loss: 0.022496 Epoch 12 ------------------------------- Train loss: 0.022481 Epoch 13 ------------------------------- Train loss: 0.020199 Epoch 14 ------------------------------- Train loss: 0.018238 Epoch 15 ------------------------------- Train loss: 0.018290 Epoch 16 ------------------------------- Train loss: 0.016427 Epoch 17 ------------------------------- Train loss: 0.015260 Epoch 18 ------------------------------- Train loss: 0.015302 Epoch 19 ------------------------------- Train loss: 0.013471 Epoch 20 ------------------------------- Train loss: 0.014010 Epoch 21 ------------------------------- Train loss: 0.012462 Epoch 22 ------------------------------- Train loss: 0.013137 Epoch 23 ------------------------------- Train loss: 0.011866 Epoch 24 ------------------------------- Train loss: 0.010669 Epoch 25 ------------------------------- Train loss: 0.011627 Epoch 26 ------------------------------- Train loss: 0.011486 Epoch 27 ------------------------------- Train loss: 0.010213 Epoch 28 ------------------------------- Train loss: 0.010717 Epoch 29 ------------------------------- Train loss: 0.009978 Epoch 30 ------------------------------- Train loss: 0.009602 Epoch 31 ------------------------------- Train loss: 0.008519 Epoch 32 ------------------------------- Train loss: 0.009439 Epoch 33 ------------------------------- Train loss: 0.009631 Epoch 34 ------------------------------- Train loss: 0.008016 Epoch 35 ------------------------------- Train loss: 0.008509 Epoch 36 ------------------------------- Train loss: 0.008494 Epoch 37 ------------------------------- Train loss: 0.008626 Epoch 38 ------------------------------- Train loss: 0.008521 Epoch 39 ------------------------------- Train loss: 0.007956 Epoch 40 ------------------------------- Train loss: 0.008263 Epoch 41 ------------------------------- Train loss: 0.008098 Epoch 42 ------------------------------- Train loss: 0.007909 Epoch 43 ------------------------------- Train loss: 0.007518 Epoch 44 ------------------------------- Train loss: 0.007196 Epoch 45 ------------------------------- Train loss: 0.006658 Epoch 46 ------------------------------- Train loss: 0.006711 Epoch 47 ------------------------------- Train loss: 0.006644 Epoch 48 ------------------------------- Train loss: 0.005905 Epoch 49 ------------------------------- Train loss: 0.006352 Epoch 50 ------------------------------- Train loss: 0.006229 Epoch 51 ------------------------------- Train loss: 0.005904 Epoch 52 ------------------------------- Train loss: 0.005599 Epoch 53 ------------------------------- Train loss: 0.005511 Epoch 54 ------------------------------- Train loss: 0.005605 Epoch 55 ------------------------------- Train loss: 0.005563 Epoch 56 ------------------------------- Train loss: 0.005647 Epoch 57 ------------------------------- Train loss: 0.005147 Epoch 58 ------------------------------- Train loss: 0.006032 Epoch 59 ------------------------------- Train loss: 0.005884 Epoch 60 ------------------------------- Train loss: 0.006103 Epoch 61 ------------------------------- Train loss: 0.005790 Epoch 62 ------------------------------- Train loss: 0.006245 Epoch 63 ------------------------------- Train loss: 0.005435 Epoch 64 ------------------------------- Train loss: 0.005679 Epoch 65 ------------------------------- Train loss: 0.005226 Epoch 66 ------------------------------- Train loss: 0.005258 Epoch 67 ------------------------------- Train loss: 0.005204 Epoch 68 ------------------------------- Train loss: 0.004861 Epoch 69 ------------------------------- Train loss: 0.004615 Epoch 70 ------------------------------- Train loss: 0.004632 Epoch 71 ------------------------------- Train loss: 0.004350 Epoch 72 ------------------------------- Train loss: 0.004546 Epoch 73 ------------------------------- Train loss: 0.004278 Epoch 74 ------------------------------- Train loss: 0.004336 Epoch 75 ------------------------------- Train loss: 0.004204 Epoch 76 ------------------------------- Train loss: 0.004179 Epoch 77 ------------------------------- Train loss: 0.004217 Epoch 78 ------------------------------- Train loss: 0.004044 Epoch 79 ------------------------------- Train loss: 0.003927 Epoch 80 ------------------------------- Train loss: 0.003926 Epoch 81 ------------------------------- Train loss: 0.004141 Epoch 82 ------------------------------- Train loss: 0.003970 Epoch 83 ------------------------------- Train loss: 0.004077 Epoch 84 ------------------------------- Train loss: 0.003887 Epoch 85 ------------------------------- Train loss: 0.004213 Epoch 86 ------------------------------- Train loss: 0.004135 Epoch 87 ------------------------------- Train loss: 0.004355 Epoch 88 ------------------------------- Train loss: 0.004201 Epoch 89 ------------------------------- Train loss: 0.003920 Epoch 90 ------------------------------- Train loss: 0.003826 Epoch 91 ------------------------------- Train loss: 0.003749 Epoch 92 ------------------------------- Train loss: 0.003595 Epoch 93 ------------------------------- Train loss: 0.003618 Epoch 94 ------------------------------- Train loss: 0.003475 Epoch 95 ------------------------------- Train loss: 0.003513 Epoch 96 ------------------------------- Train loss: 0.003407 Epoch 97 ------------------------------- Train loss: 0.003451 Epoch 98 ------------------------------- Train loss: 0.003242 Epoch 99 ------------------------------- Train loss: 0.003321 Epoch 100 ------------------------------- Train loss: 0.003201 Epoch 101 ------------------------------- Train loss: 0.003324 Epoch 102 ------------------------------- Train loss: 0.003249 Epoch 103 ------------------------------- Train loss: 0.003413 Epoch 104 ------------------------------- Train loss: 0.003334 Epoch 105 ------------------------------- Train loss: 0.003207 Epoch 106 ------------------------------- Train loss: 0.003242 Epoch 107 ------------------------------- Train loss: 0.003263 Epoch 108 ------------------------------- Train loss: 0.002999 Epoch 109 ------------------------------- Train loss: 0.003010 Epoch 110 ------------------------------- Train loss: 0.003058 Epoch 111 ------------------------------- Train loss: 0.003036 Epoch 112 ------------------------------- Train loss: 0.002988 Epoch 113 ------------------------------- Train loss: 0.002870 Epoch 114 ------------------------------- Train loss: 0.003068 Epoch 115 ------------------------------- Train loss: 0.003269 Epoch 116 ------------------------------- Train loss: 0.003066 Epoch 117 ------------------------------- Train loss: 0.003936 Epoch 118 ------------------------------- Train loss: 0.003567 Epoch 119 ------------------------------- Train loss: 0.003131 Epoch 120 ------------------------------- Train loss: 0.003587 Epoch 121 ------------------------------- Train loss: 0.003687 Epoch 122 ------------------------------- Train loss: 0.003479 Epoch 123 ------------------------------- Train loss: 0.003329 Epoch 124 ------------------------------- Train loss: 0.003154 Epoch 125 ------------------------------- Train loss: 0.003134 Epoch 126 ------------------------------- Train loss: 0.003226 Epoch 127 ------------------------------- Train loss: 0.003233 Epoch 128 ------------------------------- Train loss: 0.002905 Epoch 129 ------------------------------- Train loss: 0.003479 Epoch 130 ------------------------------- Train loss: 0.004132 Epoch 131 ------------------------------- Train loss: 0.003944 Epoch 132 ------------------------------- Train loss: 0.003420 Epoch 133 ------------------------------- Train loss: 0.003377 Epoch 134 ------------------------------- Train loss: 0.003219 Epoch 135 ------------------------------- Train loss: 0.003396 Epoch 136 ------------------------------- Train loss: 0.003134 Epoch 137 ------------------------------- Train loss: 0.003230 Epoch 138 ------------------------------- Train loss: 0.002960 Epoch 139 ------------------------------- Train loss: 0.003015 Epoch 140 ------------------------------- Train loss: 0.002822 Epoch 141 ------------------------------- Train loss: 0.002748 Epoch 142 ------------------------------- Train loss: 0.002651 Epoch 143 ------------------------------- Train loss: 0.002638 Epoch 144 ------------------------------- Train loss: 0.002588 Epoch 145 ------------------------------- Train loss: 0.002551 Epoch 146 ------------------------------- Train loss: 0.002564 Epoch 147 ------------------------------- Train loss: 0.002484 Epoch 148 ------------------------------- Train loss: 0.002433 Epoch 149 ------------------------------- Train loss: 0.002433 Epoch 150 ------------------------------- Train loss: 0.002350 Epoch 151 ------------------------------- Train loss: 0.002358 Epoch 152 ------------------------------- Train loss: 0.002301 Epoch 153 ------------------------------- Train loss: 0.002226 Epoch 154 ------------------------------- Train loss: 0.002335 Epoch 155 ------------------------------- Train loss: 0.002267 Epoch 156 ------------------------------- Train loss: 0.002262 Epoch 157 ------------------------------- Train loss: 0.002218 Epoch 158 ------------------------------- Train loss: 0.002250 Epoch 159 ------------------------------- Train loss: 0.002194 Epoch 160 ------------------------------- Train loss: 0.002162 Epoch 161 ------------------------------- Train loss: 0.002241 Epoch 162 ------------------------------- Train loss: 0.002164 Epoch 163 ------------------------------- Train loss: 0.002126 Epoch 164 ------------------------------- Train loss: 0.002257 Epoch 165 ------------------------------- Train loss: 0.002161 Epoch 166 ------------------------------- Train loss: 0.002238 Epoch 167 ------------------------------- Train loss: 0.002107 Epoch 168 ------------------------------- Train loss: 0.002634 Epoch 169 ------------------------------- Train loss: 0.002447 Epoch 170 ------------------------------- Train loss: 0.002342 Epoch 171 ------------------------------- Train loss: 0.002703 Epoch 172 ------------------------------- Train loss: 0.002620 Epoch 173 ------------------------------- Train loss: 0.002537 Epoch 174 ------------------------------- Train loss: 0.002358 Epoch 175 ------------------------------- Train loss: 0.002213 Epoch 176 ------------------------------- Train loss: 0.002243 Epoch 177 ------------------------------- Train loss: 0.002179 Epoch 178 ------------------------------- Train loss: 0.002263 Epoch 179 ------------------------------- Train loss: 0.002117 Epoch 180 ------------------------------- Train loss: 0.002102 Epoch 181 ------------------------------- Train loss: 0.002042 Epoch 182 ------------------------------- Train loss: 0.002114 Epoch 183 ------------------------------- Train loss: 0.002037 Epoch 184 ------------------------------- Train loss: 0.002059 Epoch 185 ------------------------------- Train loss: 0.001963 Epoch 186 ------------------------------- Train loss: 0.001970 Epoch 187 ------------------------------- Train loss: 0.001936 Epoch 188 ------------------------------- Train loss: 0.001960 Epoch 189 ------------------------------- Train loss: 0.002015 Epoch 190 ------------------------------- Train loss: 0.001953 Epoch 191 ------------------------------- Train loss: 0.002030 Epoch 192 ------------------------------- Train loss: 0.001905 Epoch 193 ------------------------------- Train loss: 0.002090 Epoch 194 ------------------------------- Train loss: 0.002027 Epoch 195 ------------------------------- Train loss: 0.002084 Epoch 196 ------------------------------- Train loss: 0.002092 Epoch 197 ------------------------------- Train loss: 0.002323 Epoch 198 ------------------------------- Train loss: 0.002340 Epoch 199 ------------------------------- Train loss: 0.002519 Epoch 200 ------------------------------- Train loss: 0.002291 Epoch 201 ------------------------------- Train loss: 0.002199 Epoch 202 ------------------------------- Train loss: 0.002262 Epoch 203 ------------------------------- Train loss: 0.002136 Epoch 204 ------------------------------- Train loss: 0.002153 Epoch 205 ------------------------------- Train loss: 0.002183 Epoch 206 ------------------------------- Train loss: 0.002088 Epoch 207 ------------------------------- Train loss: 0.002062 Epoch 208 ------------------------------- Train loss: 0.001934 Epoch 209 ------------------------------- Train loss: 0.001975 Epoch 210 ------------------------------- Train loss: 0.001952 Epoch 211 ------------------------------- Train loss: 0.001943 Epoch 212 ------------------------------- Train loss: 0.001883 Epoch 213 ------------------------------- Train loss: 0.001936 Epoch 214 ------------------------------- Train loss: 0.001813 Epoch 215 ------------------------------- Train loss: 0.001870 Epoch 216 ------------------------------- Train loss: 0.001805 Epoch 217 ------------------------------- Train loss: 0.001817 Epoch 218 ------------------------------- Train loss: 0.001791 Epoch 219 ------------------------------- Train loss: 0.001743 Epoch 220 ------------------------------- Train loss: 0.001732 Epoch 221 ------------------------------- Train loss: 0.001757 Epoch 222 ------------------------------- Train loss: 0.001728 Epoch 223 ------------------------------- Train loss: 0.001706 Epoch 224 ------------------------------- Train loss: 0.001711 Epoch 225 ------------------------------- Train loss: 0.001723 Epoch 226 ------------------------------- Train loss: 0.001705 Epoch 227 ------------------------------- Train loss: 0.001804 Epoch 228 ------------------------------- Train loss: 0.001756 Epoch 229 ------------------------------- Train loss: 0.001674 Epoch 230 ------------------------------- Train loss: 0.001715 Epoch 231 ------------------------------- Train loss: 0.001810 Epoch 232 ------------------------------- Train loss: 0.001901 Epoch 233 ------------------------------- Train loss: 0.001664 Epoch 234 ------------------------------- Train loss: 0.002088 Epoch 235 ------------------------------- Train loss: 0.002065 Epoch 236 ------------------------------- Train loss: 0.001965 Epoch 237 ------------------------------- Train loss: 0.001829 Epoch 238 ------------------------------- Train loss: 0.002123 Epoch 239 ------------------------------- Train loss: 0.001953 Epoch 240 ------------------------------- Train loss: 0.002015 Epoch 241 ------------------------------- Train loss: 0.002339 Epoch 242 ------------------------------- Train loss: 0.002196 Epoch 243 ------------------------------- Train loss: 0.002151 Epoch 244 ------------------------------- Train loss: 0.002147 Epoch 245 ------------------------------- Train loss: 0.001947 Epoch 246 ------------------------------- Train loss: 0.002042 Epoch 247 ------------------------------- Train loss: 0.001949 Epoch 248 ------------------------------- Train loss: 0.001948 Epoch 249 ------------------------------- Train loss: 0.001986 Epoch 250 ------------------------------- Train loss: 0.001920 Epoch 251 ------------------------------- Train loss: 0.001770 Epoch 252 ------------------------------- Train loss: 0.001901 Epoch 253 ------------------------------- Train loss: 0.001877 Epoch 254 ------------------------------- Train loss: 0.001814 Epoch 255 ------------------------------- Train loss: 0.001729 Epoch 256 ------------------------------- Train loss: 0.001757 Epoch 257 ------------------------------- Train loss: 0.001657 Epoch 258 ------------------------------- Train loss: 0.001690 Epoch 259 ------------------------------- Train loss: 0.001660 Epoch 260 ------------------------------- Train loss: 0.001741 Epoch 261 ------------------------------- Train loss: 0.001644 Epoch 262 ------------------------------- Train loss: 0.001766 Epoch 263 ------------------------------- Train loss: 0.001649 Epoch 264 ------------------------------- Train loss: 0.001684 Epoch 265 ------------------------------- Train loss: 0.001658 Epoch 266 ------------------------------- Train loss: 0.001654 Epoch 267 ------------------------------- Train loss: 0.001743 Epoch 268 ------------------------------- Train loss: 0.001639 Epoch 269 ------------------------------- Train loss: 0.001647 Epoch 270 ------------------------------- Train loss: 0.001660 Epoch 271 ------------------------------- Train loss: 0.001696 Epoch 272 ------------------------------- Train loss: 0.001580 Epoch 273 ------------------------------- Train loss: 0.001659 Epoch 274 ------------------------------- Train loss: 0.001633 Epoch 275 ------------------------------- Train loss: 0.001651 Epoch 276 ------------------------------- Train loss: 0.001619 Epoch 277 ------------------------------- Train loss: 0.001548 Epoch 278 ------------------------------- Train loss: 0.001604 Epoch 279 ------------------------------- Train loss: 0.001572 Epoch 280 ------------------------------- Train loss: 0.001544 Epoch 281 ------------------------------- Train loss: 0.001615 Epoch 282 ------------------------------- Train loss: 0.001555 Epoch 283 ------------------------------- Train loss: 0.001541 Epoch 284 ------------------------------- Train loss: 0.001503 Epoch 285 ------------------------------- Train loss: 0.001522 Epoch 286 ------------------------------- Train loss: 0.001494 Epoch 287 ------------------------------- Train loss: 0.001481 Epoch 288 ------------------------------- Train loss: 0.001504 Epoch 289 ------------------------------- Train loss: 0.001432 Epoch 290 ------------------------------- Train loss: 0.001557 Epoch 291 ------------------------------- Train loss: 0.001522 Epoch 292 ------------------------------- Train loss: 0.001469 Epoch 293 ------------------------------- Train loss: 0.001514 Epoch 294 ------------------------------- Train loss: 0.001445 Epoch 295 ------------------------------- Train loss: 0.001489 Epoch 296 ------------------------------- Train loss: 0.001509 Epoch 297 ------------------------------- Train loss: 0.001492 Epoch 298 ------------------------------- Train loss: 0.001453 Epoch 299 ------------------------------- Train loss: 0.001436 Epoch 300 ------------------------------- Train loss: 0.001532 Epoch 301 ------------------------------- Train loss: 0.001548 Epoch 302 ------------------------------- Train loss: 0.001499 Epoch 303 ------------------------------- Train loss: 0.001477 Epoch 304 ------------------------------- Train loss: 0.001475 Epoch 305 ------------------------------- Train loss: 0.001397 Epoch 306 ------------------------------- Train loss: 0.001428 Epoch 307 ------------------------------- Train loss: 0.001423 Epoch 308 ------------------------------- Train loss: 0.001412 Epoch 309 ------------------------------- Train loss: 0.001452 Epoch 310 ------------------------------- Train loss: 0.001431 Epoch 311 ------------------------------- Train loss: 0.001446 Epoch 312 ------------------------------- Train loss: 0.001415 Epoch 313 ------------------------------- Train loss: 0.001400 Epoch 314 ------------------------------- Train loss: 0.001485 Epoch 315 ------------------------------- Train loss: 0.001599 Epoch 316 ------------------------------- Train loss: 0.001516 Epoch 317 ------------------------------- Train loss: 0.001457 Epoch 318 ------------------------------- Train loss: 0.001500 Epoch 319 ------------------------------- Train loss: 0.001454 Epoch 320 ------------------------------- Train loss: 0.001399 Epoch 321 ------------------------------- Train loss: 0.001663 Epoch 322 ------------------------------- Train loss: 0.001687 Epoch 323 ------------------------------- Train loss: 0.001524 Epoch 324 ------------------------------- Train loss: 0.001761 Epoch 325 ------------------------------- Train loss: 0.001611 Epoch 326 ------------------------------- Train loss: 0.002054 Epoch 327 ------------------------------- Train loss: 0.001913 Epoch 328 ------------------------------- Train loss: 0.001994 Epoch 329 ------------------------------- Train loss: 0.001956 Epoch 330 ------------------------------- Train loss: 0.001806 Epoch 331 ------------------------------- Train loss: 0.001755 Epoch 332 ------------------------------- Train loss: 0.001642 Epoch 333 ------------------------------- Train loss: 0.001646 Epoch 334 ------------------------------- Train loss: 0.001641 Epoch 335 ------------------------------- Train loss: 0.001575 Epoch 336 ------------------------------- Train loss: 0.001536 Epoch 337 ------------------------------- Train loss: 0.001522 Epoch 338 ------------------------------- Train loss: 0.001502 Epoch 339 ------------------------------- Train loss: 0.001479 Epoch 340 ------------------------------- Train loss: 0.001435 Epoch 341 ------------------------------- Train loss: 0.001515 Epoch 342 ------------------------------- Train loss: 0.001381 Epoch 343 ------------------------------- Train loss: 0.001443 Epoch 344 ------------------------------- Train loss: 0.001411 Epoch 345 ------------------------------- Train loss: 0.001382 Epoch 346 ------------------------------- Train loss: 0.001324 Epoch 347 ------------------------------- Train loss: 0.001341 Epoch 348 ------------------------------- Train loss: 0.001321 Epoch 349 ------------------------------- Train loss: 0.001340 Epoch 350 ------------------------------- Train loss: 0.001341 Epoch 351 ------------------------------- Train loss: 0.001341 Epoch 352 ------------------------------- Train loss: 0.001323 Epoch 353 ------------------------------- Train loss: 0.001355 Epoch 354 ------------------------------- Train loss: 0.001377 Epoch 355 ------------------------------- Train loss: 0.001313 Epoch 356 ------------------------------- Train loss: 0.001325 Epoch 357 ------------------------------- Train loss: 0.001344 Epoch 358 ------------------------------- Train loss: 0.001294 Epoch 359 ------------------------------- Train loss: 0.001296 Epoch 360 ------------------------------- Train loss: 0.001377 Epoch 361 ------------------------------- Train loss: 0.001291 Epoch 362 ------------------------------- Train loss: 0.001482 Epoch 363 ------------------------------- Train loss: 0.001436 Epoch 364 ------------------------------- Train loss: 0.001476 Epoch 365 ------------------------------- Train loss: 0.001368 Epoch 366 ------------------------------- Train loss: 0.001990 Epoch 367 ------------------------------- Train loss: 0.001963 Epoch 368 ------------------------------- Train loss: 0.002019 Epoch 369 ------------------------------- Train loss: 0.002024 Epoch 370 ------------------------------- Train loss: 0.001985 Epoch 371 ------------------------------- Train loss: 0.002026 Epoch 372 ------------------------------- Train loss: 0.001886 Epoch 373 ------------------------------- Train loss: 0.001857 Epoch 374 ------------------------------- Train loss: 0.001835 Epoch 375 ------------------------------- Train loss: 0.001742 Epoch 376 ------------------------------- Train loss: 0.001690 Epoch 377 ------------------------------- Train loss: 0.001664 Epoch 378 ------------------------------- Train loss: 0.001570 Epoch 379 ------------------------------- Train loss: 0.001589 Epoch 380 ------------------------------- Train loss: 0.001491 Epoch 381 ------------------------------- Train loss: 0.001498 Epoch 382 ------------------------------- Train loss: 0.001429 Epoch 383 ------------------------------- Train loss: 0.001389 Epoch 384 ------------------------------- Train loss: 0.001384 Epoch 385 ------------------------------- Train loss: 0.001384 Epoch 386 ------------------------------- Train loss: 0.001364 Epoch 387 ------------------------------- Train loss: 0.001339 Epoch 388 ------------------------------- Train loss: 0.001341 Epoch 389 ------------------------------- Train loss: 0.001302 Epoch 390 ------------------------------- Train loss: 0.001328 Epoch 391 ------------------------------- Train loss: 0.001316 Epoch 392 ------------------------------- Train loss: 0.001259 Epoch 393 ------------------------------- Train loss: 0.001281 Epoch 394 ------------------------------- Train loss: 0.001232 Epoch 395 ------------------------------- Train loss: 0.001230 Epoch 396 ------------------------------- Train loss: 0.001244 Epoch 397 ------------------------------- Train loss: 0.001216 Epoch 398 ------------------------------- Train loss: 0.001214 Epoch 399 ------------------------------- Train loss: 0.001217 Epoch 400 ------------------------------- Train loss: 0.001240 Epoch 401 ------------------------------- Train loss: 0.001213 Epoch 402 ------------------------------- Train loss: 0.001212 Epoch 403 ------------------------------- Train loss: 0.001194 Epoch 404 ------------------------------- Train loss: 0.001192 Epoch 405 ------------------------------- Train loss: 0.001205 Epoch 406 ------------------------------- Train loss: 0.001168 Epoch 407 ------------------------------- Train loss: 0.001274 Epoch 408 ------------------------------- Train loss: 0.001315 Epoch 409 ------------------------------- Train loss: 0.001194 Epoch 410 ------------------------------- Train loss: 0.001202 Epoch 411 ------------------------------- Train loss: 0.001267 Epoch 412 ------------------------------- Train loss: 0.001176 Epoch 413 ------------------------------- Train loss: 0.001254 Epoch 414 ------------------------------- Train loss: 0.001372 Epoch 415 ------------------------------- Train loss: 0.001248 Epoch 416 ------------------------------- Train loss: 0.001455 Epoch 417 ------------------------------- Train loss: 0.001343 Epoch 418 ------------------------------- Train loss: 0.001848 Epoch 419 ------------------------------- Train loss: 0.001597 Epoch 420 ------------------------------- Train loss: 0.001671 Epoch 421 ------------------------------- Train loss: 0.001561 Epoch 422 ------------------------------- Train loss: 0.001590 Epoch 423 ------------------------------- Train loss: 0.001666 Epoch 424 ------------------------------- Train loss: 0.001484 Epoch 425 ------------------------------- Train loss: 0.001547 Epoch 426 ------------------------------- Train loss: 0.001518 Epoch 427 ------------------------------- Train loss: 0.001423 Epoch 428 ------------------------------- Train loss: 0.001449 Epoch 429 ------------------------------- Train loss: 0.001448 Epoch 430 ------------------------------- Train loss: 0.001388 Epoch 431 ------------------------------- Train loss: 0.001408 Epoch 432 ------------------------------- Train loss: 0.001475 Epoch 433 ------------------------------- Train loss: 0.001420 Epoch 434 ------------------------------- Train loss: 0.001358 Epoch 435 ------------------------------- Train loss: 0.001316 Epoch 436 ------------------------------- Train loss: 0.001382 Epoch 437 ------------------------------- Train loss: 0.001435 Epoch 438 ------------------------------- Train loss: 0.001412 Epoch 439 ------------------------------- Train loss: 0.001343 Epoch 440 ------------------------------- Train loss: 0.001416 Epoch 441 ------------------------------- Train loss: 0.001279 Epoch 442 ------------------------------- Train loss: 0.001358 Epoch 443 ------------------------------- Train loss: 0.001264 Epoch 444 ------------------------------- Train loss: 0.001316 Epoch 445 ------------------------------- Train loss: 0.001380 Epoch 446 ------------------------------- Train loss: 0.001405 Epoch 447 ------------------------------- Train loss: 0.001370 Epoch 448 ------------------------------- Train loss: 0.001273 Epoch 449 ------------------------------- Train loss: 0.001305 Epoch 450 ------------------------------- Train loss: 0.001272 Epoch 451 ------------------------------- Train loss: 0.001280 Epoch 452 ------------------------------- Train loss: 0.001245 Epoch 453 ------------------------------- Train loss: 0.001196 Epoch 454 ------------------------------- Train loss: 0.001206 Epoch 455 ------------------------------- Train loss: 0.001347 Epoch 456 ------------------------------- Train loss: 0.001249 Epoch 457 ------------------------------- Train loss: 0.001187 Epoch 458 ------------------------------- Train loss: 0.001250 Epoch 459 ------------------------------- Train loss: 0.001275 Epoch 460 ------------------------------- Train loss: 0.001266 Epoch 461 ------------------------------- Train loss: 0.001195 Epoch 462 ------------------------------- Train loss: 0.001191 Epoch 463 ------------------------------- Train loss: 0.001291 Epoch 464 ------------------------------- Train loss: 0.001320 Epoch 465 ------------------------------- Train loss: 0.001236 Epoch 466 ------------------------------- Train loss: 0.001251 Epoch 467 ------------------------------- Train loss: 0.001184 Epoch 468 ------------------------------- Train loss: 0.001347 Epoch 469 ------------------------------- Train loss: 0.001275 Epoch 470 ------------------------------- Train loss: 0.001316 Epoch 471 ------------------------------- Train loss: 0.001185 Epoch 472 ------------------------------- Train loss: 0.001170 Epoch 473 ------------------------------- Train loss: 0.001190 Epoch 474 ------------------------------- Train loss: 0.001234 Epoch 475 ------------------------------- Train loss: 0.001220 Epoch 476 ------------------------------- Train loss: 0.001184 Epoch 477 ------------------------------- Train loss: 0.001180 Epoch 478 ------------------------------- Train loss: 0.001144 Epoch 479 ------------------------------- Train loss: 0.001277 Epoch 480 ------------------------------- Train loss: 0.001198 Epoch 481 ------------------------------- Train loss: 0.001211 Epoch 482 ------------------------------- Train loss: 0.001151 Epoch 483 ------------------------------- Train loss: 0.001118 Epoch 484 ------------------------------- Train loss: 0.001120 Epoch 485 ------------------------------- Train loss: 0.001116 Epoch 486 ------------------------------- Train loss: 0.001082 Epoch 487 ------------------------------- Train loss: 0.001074 Epoch 488 ------------------------------- Train loss: 0.001089 Epoch 489 ------------------------------- Train loss: 0.001100 Epoch 490 ------------------------------- Train loss: 0.001080 Epoch 491 ------------------------------- Train loss: 0.001055 Epoch 492 ------------------------------- Train loss: 0.001073 Epoch 493 ------------------------------- Train loss: 0.001080 Epoch 494 ------------------------------- Train loss: 0.001052 Epoch 495 ------------------------------- Train loss: 0.001067 Epoch 496 ------------------------------- Train loss: 0.001033 Epoch 497 ------------------------------- Train loss: 0.001058 Epoch 498 ------------------------------- Train loss: 0.001080 Epoch 499 ------------------------------- Train loss: 0.001064 Epoch 500 ------------------------------- Train loss: 0.001044 Done!
import soundfile as sf
model.to('cpu')
with torch.no_grad():
Xt, sr=librosa.load('//content/drive/MyDrive/deep learning/Assignment2/train_dirty_male.wav', sr= None)
Xt=librosa.stft(Xt, n_fft=1024, hop_length=512)
Xt_abs = np.abs(Xt)
Xt_abs.astype(np.float32)
Yt_abs = np.asarray(model(torch.tensor(Xt_abs.T))).T
Yt = (Xt/ Xt_abs) * Yt_abs
Yt = librosa.istft( Yt, hop_length=512)
sf.write('Qs1_train_clean_male_recons_v1.wav', Yt, sr)
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:1949: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
Now, I am going to split my model into training and validation, and train it on only the training data, and get the results on the whole dataset
#Lets train a new model with setting aside part of our training set as validation set, and compute SNR
s, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/train_clean_male.wav', sr=None)
S=librosa.stft(s, n_fft=1024, hop_length=512)
sn, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/train_dirty_male.wav', sr=None)
X=librosa.stft(sn, n_fft=1024, hop_length=512)
batch_size = 2000
X = X.T
S = S.T
s_mag = np.abs(S)
x_mag = np.abs(X)
X = torch.from_numpy(x_mag)
S = torch.from_numpy(s_mag)
train_dataset = TensorDataset(X, S)
dirty_train, dirty_validation = torch.utils.data.random_split(train_dataset, [2000, 459], generator=torch.Generator().manual_seed(42))
train_dataloader = DataLoader(dirty_train, batch_size=batch_size, shuffle=True)
validation_dataloader = DataLoader(dirty_validation, batch_size=batch_size, shuffle=True)
print(len(train_dataloader.dataset))
print(len(validation_dataloader.dataset))
2000 459
model = NeuralNetwork().to(device)
loss_fn = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
model.apply(xavier_initializer)
scheduler = StepLR(optimizer, step_size=50, gamma=0.1)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_. This is separate from the ipykernel package so we can avoid doing imports until
train_epochs(train_dataloader, model, loss_fn, optimizer)
Epoch 1 ------------------------------- Train loss: 0.105531 Epoch 2 ------------------------------- Train loss: 0.085627 Epoch 3 ------------------------------- Train loss: 0.071953 Epoch 4 ------------------------------- Train loss: 0.062219 Epoch 5 ------------------------------- Train loss: 0.054606 Epoch 6 ------------------------------- Train loss: 0.049248 Epoch 7 ------------------------------- Train loss: 0.045140 Epoch 8 ------------------------------- Train loss: 0.041634 Epoch 9 ------------------------------- Train loss: 0.038545 Epoch 10 ------------------------------- Train loss: 0.035752 Epoch 11 ------------------------------- Train loss: 0.033412 Epoch 12 ------------------------------- Train loss: 0.031476 Epoch 13 ------------------------------- Train loss: 0.029801 Epoch 14 ------------------------------- Train loss: 0.028250 Epoch 15 ------------------------------- Train loss: 0.026812 Epoch 16 ------------------------------- Train loss: 0.025522 Epoch 17 ------------------------------- Train loss: 0.024371 Epoch 18 ------------------------------- Train loss: 0.023314 Epoch 19 ------------------------------- Train loss: 0.022349 Epoch 20 ------------------------------- Train loss: 0.021456 Epoch 21 ------------------------------- Train loss: 0.020615 Epoch 22 ------------------------------- Train loss: 0.019822 Epoch 23 ------------------------------- Train loss: 0.019092 Epoch 24 ------------------------------- Train loss: 0.018420 Epoch 25 ------------------------------- Train loss: 0.017781 Epoch 26 ------------------------------- Train loss: 0.017164 Epoch 27 ------------------------------- Train loss: 0.016575 Epoch 28 ------------------------------- Train loss: 0.016017 Epoch 29 ------------------------------- Train loss: 0.015497 Epoch 30 ------------------------------- Train loss: 0.015020 Epoch 31 ------------------------------- Train loss: 0.014566 Epoch 32 ------------------------------- Train loss: 0.014137 Epoch 33 ------------------------------- Train loss: 0.013730 Epoch 34 ------------------------------- Train loss: 0.013340 Epoch 35 ------------------------------- Train loss: 0.012986 Epoch 36 ------------------------------- Train loss: 0.012755 Epoch 37 ------------------------------- Train loss: 0.012995 Epoch 38 ------------------------------- Train loss: 0.013978 Epoch 39 ------------------------------- Train loss: 0.011949 Epoch 40 ------------------------------- Train loss: 0.013307 Epoch 41 ------------------------------- Train loss: 0.012280 Epoch 42 ------------------------------- Train loss: 0.012611 Epoch 43 ------------------------------- Train loss: 0.011121 Epoch 44 ------------------------------- Train loss: 0.012278 Epoch 45 ------------------------------- Train loss: 0.010706 Epoch 46 ------------------------------- Train loss: 0.011630 Epoch 47 ------------------------------- Train loss: 0.010361 Epoch 48 ------------------------------- Train loss: 0.010932 Epoch 49 ------------------------------- Train loss: 0.009958 Epoch 50 ------------------------------- Train loss: 0.010187 Epoch 51 ------------------------------- Train loss: 0.009875 Epoch 52 ------------------------------- Train loss: 0.009559 Epoch 53 ------------------------------- Train loss: 0.009711 Epoch 54 ------------------------------- Train loss: 0.009056 Epoch 55 ------------------------------- Train loss: 0.009250 Epoch 56 ------------------------------- Train loss: 0.008892 Epoch 57 ------------------------------- Train loss: 0.008848 Epoch 58 ------------------------------- Train loss: 0.008571 Epoch 59 ------------------------------- Train loss: 0.008450 Epoch 60 ------------------------------- Train loss: 0.008372 Epoch 61 ------------------------------- Train loss: 0.008192 Epoch 62 ------------------------------- Train loss: 0.008077 Epoch 63 ------------------------------- Train loss: 0.007882 Epoch 64 ------------------------------- Train loss: 0.007833 Epoch 65 ------------------------------- Train loss: 0.007663 Epoch 66 ------------------------------- Train loss: 0.007591 Epoch 67 ------------------------------- Train loss: 0.007415 Epoch 68 ------------------------------- Train loss: 0.007348 Epoch 69 ------------------------------- Train loss: 0.007237 Epoch 70 ------------------------------- Train loss: 0.007119 Epoch 71 ------------------------------- Train loss: 0.007012 Epoch 72 ------------------------------- Train loss: 0.006900 Epoch 73 ------------------------------- Train loss: 0.006835 Epoch 74 ------------------------------- Train loss: 0.006700 Epoch 75 ------------------------------- Train loss: 0.006624 Epoch 76 ------------------------------- Train loss: 0.006536 Epoch 77 ------------------------------- Train loss: 0.006439 Epoch 78 ------------------------------- Train loss: 0.006362 Epoch 79 ------------------------------- Train loss: 0.006272 Epoch 80 ------------------------------- Train loss: 0.006189 Epoch 81 ------------------------------- Train loss: 0.006115 Epoch 82 ------------------------------- Train loss: 0.006033 Epoch 83 ------------------------------- Train loss: 0.005954 Epoch 84 ------------------------------- Train loss: 0.005890 Epoch 85 ------------------------------- Train loss: 0.005810 Epoch 86 ------------------------------- Train loss: 0.005728 Epoch 87 ------------------------------- Train loss: 0.005665 Epoch 88 ------------------------------- Train loss: 0.005606 Epoch 89 ------------------------------- Train loss: 0.005538 Epoch 90 ------------------------------- Train loss: 0.005474 Epoch 91 ------------------------------- Train loss: 0.005411 Epoch 92 ------------------------------- Train loss: 0.005347 Epoch 93 ------------------------------- Train loss: 0.005293 Epoch 94 ------------------------------- Train loss: 0.005241 Epoch 95 ------------------------------- Train loss: 0.005229 Epoch 96 ------------------------------- Train loss: 0.005236 Epoch 97 ------------------------------- Train loss: 0.005487 Epoch 98 ------------------------------- Train loss: 0.005303 Epoch 99 ------------------------------- Train loss: 0.005379 Epoch 100 ------------------------------- Train loss: 0.005661 Epoch 101 ------------------------------- Train loss: 0.006952 Epoch 102 ------------------------------- Train loss: 0.005569 Epoch 103 ------------------------------- Train loss: 0.006884 Epoch 104 ------------------------------- Train loss: 0.005851 Epoch 105 ------------------------------- Train loss: 0.006521 Epoch 106 ------------------------------- Train loss: 0.005573 Epoch 107 ------------------------------- Train loss: 0.006217 Epoch 108 ------------------------------- Train loss: 0.005284 Epoch 109 ------------------------------- Train loss: 0.005885 Epoch 110 ------------------------------- Train loss: 0.005828 Epoch 111 ------------------------------- Train loss: 0.005368 Epoch 112 ------------------------------- Train loss: 0.005971 Epoch 113 ------------------------------- Train loss: 0.004996 Epoch 114 ------------------------------- Train loss: 0.005149 Epoch 115 ------------------------------- Train loss: 0.005586 Epoch 116 ------------------------------- Train loss: 0.005082 Epoch 117 ------------------------------- Train loss: 0.004843 Epoch 118 ------------------------------- Train loss: 0.005147 Epoch 119 ------------------------------- Train loss: 0.004623 Epoch 120 ------------------------------- Train loss: 0.004871 Epoch 121 ------------------------------- Train loss: 0.004558 Epoch 122 ------------------------------- Train loss: 0.004467 Epoch 123 ------------------------------- Train loss: 0.004563 Epoch 124 ------------------------------- Train loss: 0.004334 Epoch 125 ------------------------------- Train loss: 0.004381 Epoch 126 ------------------------------- Train loss: 0.004335 Epoch 127 ------------------------------- Train loss: 0.004216 Epoch 128 ------------------------------- Train loss: 0.004269 Epoch 129 ------------------------------- Train loss: 0.004084 Epoch 130 ------------------------------- Train loss: 0.004087 Epoch 131 ------------------------------- Train loss: 0.004114 Epoch 132 ------------------------------- Train loss: 0.003965 Epoch 133 ------------------------------- Train loss: 0.003928 Epoch 134 ------------------------------- Train loss: 0.003930 Epoch 135 ------------------------------- Train loss: 0.003851 Epoch 136 ------------------------------- Train loss: 0.003835 Epoch 137 ------------------------------- Train loss: 0.003809 Epoch 138 ------------------------------- Train loss: 0.003752 Epoch 139 ------------------------------- Train loss: 0.003719 Epoch 140 ------------------------------- Train loss: 0.003695 Epoch 141 ------------------------------- Train loss: 0.003676 Epoch 142 ------------------------------- Train loss: 0.003635 Epoch 143 ------------------------------- Train loss: 0.003585 Epoch 144 ------------------------------- Train loss: 0.003572 Epoch 145 ------------------------------- Train loss: 0.003547 Epoch 146 ------------------------------- Train loss: 0.003522 Epoch 147 ------------------------------- Train loss: 0.003483 Epoch 148 ------------------------------- Train loss: 0.003444 Epoch 149 ------------------------------- Train loss: 0.003430 Epoch 150 ------------------------------- Train loss: 0.003400 Epoch 151 ------------------------------- Train loss: 0.003386 Epoch 152 ------------------------------- Train loss: 0.003362 Epoch 153 ------------------------------- Train loss: 0.003355 Epoch 154 ------------------------------- Train loss: 0.003352 Epoch 155 ------------------------------- Train loss: 0.003385 Epoch 156 ------------------------------- Train loss: 0.003433 Epoch 157 ------------------------------- Train loss: 0.003659 Epoch 158 ------------------------------- Train loss: 0.003598 Epoch 159 ------------------------------- Train loss: 0.003618 Epoch 160 ------------------------------- Train loss: 0.003303 Epoch 161 ------------------------------- Train loss: 0.003191 Epoch 162 ------------------------------- Train loss: 0.003223 Epoch 163 ------------------------------- Train loss: 0.003276 Epoch 164 ------------------------------- Train loss: 0.003475 Epoch 165 ------------------------------- Train loss: 0.003318 Epoch 166 ------------------------------- Train loss: 0.003198 Epoch 167 ------------------------------- Train loss: 0.003075 Epoch 168 ------------------------------- Train loss: 0.003109 Epoch 169 ------------------------------- Train loss: 0.003305 Epoch 170 ------------------------------- Train loss: 0.003221 Epoch 171 ------------------------------- Train loss: 0.003196 Epoch 172 ------------------------------- Train loss: 0.003028 Epoch 173 ------------------------------- Train loss: 0.002993 Epoch 174 ------------------------------- Train loss: 0.003069 Epoch 175 ------------------------------- Train loss: 0.003098 Epoch 176 ------------------------------- Train loss: 0.003179 Epoch 177 ------------------------------- Train loss: 0.003010 Epoch 178 ------------------------------- Train loss: 0.002932 Epoch 179 ------------------------------- Train loss: 0.002943 Epoch 180 ------------------------------- Train loss: 0.003006 Epoch 181 ------------------------------- Train loss: 0.003082 Epoch 182 ------------------------------- Train loss: 0.002971 Epoch 183 ------------------------------- Train loss: 0.002881 Epoch 184 ------------------------------- Train loss: 0.002812 Epoch 185 ------------------------------- Train loss: 0.002840 Epoch 186 ------------------------------- Train loss: 0.002948 Epoch 187 ------------------------------- Train loss: 0.002930 Epoch 188 ------------------------------- Train loss: 0.002923 Epoch 189 ------------------------------- Train loss: 0.002791 Epoch 190 ------------------------------- Train loss: 0.002721 Epoch 191 ------------------------------- Train loss: 0.002756 Epoch 192 ------------------------------- Train loss: 0.002833 Epoch 193 ------------------------------- Train loss: 0.002958 Epoch 194 ------------------------------- Train loss: 0.002844 Epoch 195 ------------------------------- Train loss: 0.002734 Epoch 196 ------------------------------- Train loss: 0.002671 Epoch 197 ------------------------------- Train loss: 0.002751 Epoch 198 ------------------------------- Train loss: 0.002881 Epoch 199 ------------------------------- Train loss: 0.002774 Epoch 200 ------------------------------- Train loss: 0.002796 Epoch 201 ------------------------------- Train loss: 0.002868 Epoch 202 ------------------------------- Train loss: 0.002856 Epoch 203 ------------------------------- Train loss: 0.002784 Epoch 204 ------------------------------- Train loss: 0.002682 Epoch 205 ------------------------------- Train loss: 0.002834 Epoch 206 ------------------------------- Train loss: 0.002688 Epoch 207 ------------------------------- Train loss: 0.002652 Epoch 208 ------------------------------- Train loss: 0.002848 Epoch 209 ------------------------------- Train loss: 0.002655 Epoch 210 ------------------------------- Train loss: 0.002878 Epoch 211 ------------------------------- Train loss: 0.003368 Epoch 212 ------------------------------- Train loss: 0.002782 Epoch 213 ------------------------------- Train loss: 0.003475 Epoch 214 ------------------------------- Train loss: 0.002982 Epoch 215 ------------------------------- Train loss: 0.003718 Epoch 216 ------------------------------- Train loss: 0.004165 Epoch 217 ------------------------------- Train loss: 0.004360 Epoch 218 ------------------------------- Train loss: 0.003635 Epoch 219 ------------------------------- Train loss: 0.004039 Epoch 220 ------------------------------- Train loss: 0.003205 Epoch 221 ------------------------------- Train loss: 0.003602 Epoch 222 ------------------------------- Train loss: 0.003669 Epoch 223 ------------------------------- Train loss: 0.003041 Epoch 224 ------------------------------- Train loss: 0.003495 Epoch 225 ------------------------------- Train loss: 0.003189 Epoch 226 ------------------------------- Train loss: 0.003006 Epoch 227 ------------------------------- Train loss: 0.003236 Epoch 228 ------------------------------- Train loss: 0.003091 Epoch 229 ------------------------------- Train loss: 0.002904 Epoch 230 ------------------------------- Train loss: 0.002990 Epoch 231 ------------------------------- Train loss: 0.002957 Epoch 232 ------------------------------- Train loss: 0.002792 Epoch 233 ------------------------------- Train loss: 0.002883 Epoch 234 ------------------------------- Train loss: 0.002835 Epoch 235 ------------------------------- Train loss: 0.002678 Epoch 236 ------------------------------- Train loss: 0.002835 Epoch 237 ------------------------------- Train loss: 0.002861 Epoch 238 ------------------------------- Train loss: 0.002598 Epoch 239 ------------------------------- Train loss: 0.002821 Epoch 240 ------------------------------- Train loss: 0.002732 Epoch 241 ------------------------------- Train loss: 0.002562 Epoch 242 ------------------------------- Train loss: 0.002808 Epoch 243 ------------------------------- Train loss: 0.002719 Epoch 244 ------------------------------- Train loss: 0.002518 Epoch 245 ------------------------------- Train loss: 0.002750 Epoch 246 ------------------------------- Train loss: 0.002612 Epoch 247 ------------------------------- Train loss: 0.002473 Epoch 248 ------------------------------- Train loss: 0.002722 Epoch 249 ------------------------------- Train loss: 0.002512 Epoch 250 ------------------------------- Train loss: 0.002401 Epoch 251 ------------------------------- Train loss: 0.002711 Epoch 252 ------------------------------- Train loss: 0.002546 Epoch 253 ------------------------------- Train loss: 0.002423 Epoch 254 ------------------------------- Train loss: 0.002682 Epoch 255 ------------------------------- Train loss: 0.002341 Epoch 256 ------------------------------- Train loss: 0.002396 Epoch 257 ------------------------------- Train loss: 0.002517 Epoch 258 ------------------------------- Train loss: 0.002290 Epoch 259 ------------------------------- Train loss: 0.002314 Epoch 260 ------------------------------- Train loss: 0.002385 Epoch 261 ------------------------------- Train loss: 0.002225 Epoch 262 ------------------------------- Train loss: 0.002238 Epoch 263 ------------------------------- Train loss: 0.002307 Epoch 264 ------------------------------- Train loss: 0.002195 Epoch 265 ------------------------------- Train loss: 0.002167 Epoch 266 ------------------------------- Train loss: 0.002254 Epoch 267 ------------------------------- Train loss: 0.002196 Epoch 268 ------------------------------- Train loss: 0.002118 Epoch 269 ------------------------------- Train loss: 0.002199 Epoch 270 ------------------------------- Train loss: 0.002177 Epoch 271 ------------------------------- Train loss: 0.002096 Epoch 272 ------------------------------- Train loss: 0.002140 Epoch 273 ------------------------------- Train loss: 0.002163 Epoch 274 ------------------------------- Train loss: 0.002079 Epoch 275 ------------------------------- Train loss: 0.002073 Epoch 276 ------------------------------- Train loss: 0.002112 Epoch 277 ------------------------------- Train loss: 0.002071 Epoch 278 ------------------------------- Train loss: 0.002024 Epoch 279 ------------------------------- Train loss: 0.002043 Epoch 280 ------------------------------- Train loss: 0.002052 Epoch 281 ------------------------------- Train loss: 0.002011 Epoch 282 ------------------------------- Train loss: 0.001989 Epoch 283 ------------------------------- Train loss: 0.002007 Epoch 284 ------------------------------- Train loss: 0.002007 Epoch 285 ------------------------------- Train loss: 0.001980 Epoch 286 ------------------------------- Train loss: 0.001961 Epoch 287 ------------------------------- Train loss: 0.001961 Epoch 288 ------------------------------- Train loss: 0.001970 Epoch 289 ------------------------------- Train loss: 0.001960 Epoch 290 ------------------------------- Train loss: 0.001933 Epoch 291 ------------------------------- Train loss: 0.001928 Epoch 292 ------------------------------- Train loss: 0.001930 Epoch 293 ------------------------------- Train loss: 0.001927 Epoch 294 ------------------------------- Train loss: 0.001926 Epoch 295 ------------------------------- Train loss: 0.001907 Epoch 296 ------------------------------- Train loss: 0.001890 Epoch 297 ------------------------------- Train loss: 0.001888 Epoch 298 ------------------------------- Train loss: 0.001889 Epoch 299 ------------------------------- Train loss: 0.001886 Epoch 300 ------------------------------- Train loss: 0.001884 Epoch 301 ------------------------------- Train loss: 0.001879 Epoch 302 ------------------------------- Train loss: 0.001869 Epoch 303 ------------------------------- Train loss: 0.001858 Epoch 304 ------------------------------- Train loss: 0.001850 Epoch 305 ------------------------------- Train loss: 0.001842 Epoch 306 ------------------------------- Train loss: 0.001835 Epoch 307 ------------------------------- Train loss: 0.001832 Epoch 308 ------------------------------- Train loss: 0.001827 Epoch 309 ------------------------------- Train loss: 0.001823 Epoch 310 ------------------------------- Train loss: 0.001821 Epoch 311 ------------------------------- Train loss: 0.001825 Epoch 312 ------------------------------- Train loss: 0.001838 Epoch 313 ------------------------------- Train loss: 0.001859 Epoch 314 ------------------------------- Train loss: 0.001926 Epoch 315 ------------------------------- Train loss: 0.001975 Epoch 316 ------------------------------- Train loss: 0.002128 Epoch 317 ------------------------------- Train loss: 0.002040 Epoch 318 ------------------------------- Train loss: 0.002005 Epoch 319 ------------------------------- Train loss: 0.001839 Epoch 320 ------------------------------- Train loss: 0.001776 Epoch 321 ------------------------------- Train loss: 0.001804 Epoch 322 ------------------------------- Train loss: 0.001879 Epoch 323 ------------------------------- Train loss: 0.001998 Epoch 324 ------------------------------- Train loss: 0.001937 Epoch 325 ------------------------------- Train loss: 0.001875 Epoch 326 ------------------------------- Train loss: 0.001772 Epoch 327 ------------------------------- Train loss: 0.001762 Epoch 328 ------------------------------- Train loss: 0.001822 Epoch 329 ------------------------------- Train loss: 0.001862 Epoch 330 ------------------------------- Train loss: 0.001891 Epoch 331 ------------------------------- Train loss: 0.001797 Epoch 332 ------------------------------- Train loss: 0.001749 Epoch 333 ------------------------------- Train loss: 0.001756 Epoch 334 ------------------------------- Train loss: 0.001791 Epoch 335 ------------------------------- Train loss: 0.001821 Epoch 336 ------------------------------- Train loss: 0.001775 Epoch 337 ------------------------------- Train loss: 0.001743 Epoch 338 ------------------------------- Train loss: 0.001741 Epoch 339 ------------------------------- Train loss: 0.001776 Epoch 340 ------------------------------- Train loss: 0.001772 Epoch 341 ------------------------------- Train loss: 0.001735 Epoch 342 ------------------------------- Train loss: 0.001695 Epoch 343 ------------------------------- Train loss: 0.001693 Epoch 344 ------------------------------- Train loss: 0.001720 Epoch 345 ------------------------------- Train loss: 0.001732 Epoch 346 ------------------------------- Train loss: 0.001732 Epoch 347 ------------------------------- Train loss: 0.001700 Epoch 348 ------------------------------- Train loss: 0.001693 Epoch 349 ------------------------------- Train loss: 0.001718 Epoch 350 ------------------------------- Train loss: 0.001775 Epoch 351 ------------------------------- Train loss: 0.001776 Epoch 352 ------------------------------- Train loss: 0.001822 Epoch 353 ------------------------------- Train loss: 0.001813 Epoch 354 ------------------------------- Train loss: 0.001890 Epoch 355 ------------------------------- Train loss: 0.001766 Epoch 356 ------------------------------- Train loss: 0.001711 Epoch 357 ------------------------------- Train loss: 0.001687 Epoch 358 ------------------------------- Train loss: 0.001677 Epoch 359 ------------------------------- Train loss: 0.001661 Epoch 360 ------------------------------- Train loss: 0.001641 Epoch 361 ------------------------------- Train loss: 0.001647 Epoch 362 ------------------------------- Train loss: 0.001678 Epoch 363 ------------------------------- Train loss: 0.001741 Epoch 364 ------------------------------- Train loss: 0.001726 Epoch 365 ------------------------------- Train loss: 0.001722 Epoch 366 ------------------------------- Train loss: 0.001696 Epoch 367 ------------------------------- Train loss: 0.001714 Epoch 368 ------------------------------- Train loss: 0.001663 Epoch 369 ------------------------------- Train loss: 0.001630 Epoch 370 ------------------------------- Train loss: 0.001619 Epoch 371 ------------------------------- Train loss: 0.001648 Epoch 372 ------------------------------- Train loss: 0.001721 Epoch 373 ------------------------------- Train loss: 0.001703 Epoch 374 ------------------------------- Train loss: 0.001751 Epoch 375 ------------------------------- Train loss: 0.001675 Epoch 376 ------------------------------- Train loss: 0.001617 Epoch 377 ------------------------------- Train loss: 0.001579 Epoch 378 ------------------------------- Train loss: 0.001580 Epoch 379 ------------------------------- Train loss: 0.001619 Epoch 380 ------------------------------- Train loss: 0.001647 Epoch 381 ------------------------------- Train loss: 0.001699 Epoch 382 ------------------------------- Train loss: 0.001617 Epoch 383 ------------------------------- Train loss: 0.001575 Epoch 384 ------------------------------- Train loss: 0.001602 Epoch 385 ------------------------------- Train loss: 0.001636 Epoch 386 ------------------------------- Train loss: 0.001752 Epoch 387 ------------------------------- Train loss: 0.001741 Epoch 388 ------------------------------- Train loss: 0.001960 Epoch 389 ------------------------------- Train loss: 0.001894 Epoch 390 ------------------------------- Train loss: 0.001926 Epoch 391 ------------------------------- Train loss: 0.001668 Epoch 392 ------------------------------- Train loss: 0.001747 Epoch 393 ------------------------------- Train loss: 0.001801 Epoch 394 ------------------------------- Train loss: 0.001825 Epoch 395 ------------------------------- Train loss: 0.002196 Epoch 396 ------------------------------- Train loss: 0.001665 Epoch 397 ------------------------------- Train loss: 0.001795 Epoch 398 ------------------------------- Train loss: 0.002228 Epoch 399 ------------------------------- Train loss: 0.001825 Epoch 400 ------------------------------- Train loss: 0.002163 Epoch 401 ------------------------------- Train loss: 0.002026 Epoch 402 ------------------------------- Train loss: 0.002314 Epoch 403 ------------------------------- Train loss: 0.002179 Epoch 404 ------------------------------- Train loss: 0.001979 Epoch 405 ------------------------------- Train loss: 0.002149 Epoch 406 ------------------------------- Train loss: 0.001908 Epoch 407 ------------------------------- Train loss: 0.001806 Epoch 408 ------------------------------- Train loss: 0.001820 Epoch 409 ------------------------------- Train loss: 0.001927 Epoch 410 ------------------------------- Train loss: 0.001724 Epoch 411 ------------------------------- Train loss: 0.001905 Epoch 412 ------------------------------- Train loss: 0.001758 Epoch 413 ------------------------------- Train loss: 0.001732 Epoch 414 ------------------------------- Train loss: 0.001755 Epoch 415 ------------------------------- Train loss: 0.001729 Epoch 416 ------------------------------- Train loss: 0.001683 Epoch 417 ------------------------------- Train loss: 0.001626 Epoch 418 ------------------------------- Train loss: 0.001650 Epoch 419 ------------------------------- Train loss: 0.001634 Epoch 420 ------------------------------- Train loss: 0.001614 Epoch 421 ------------------------------- Train loss: 0.001572 Epoch 422 ------------------------------- Train loss: 0.001601 Epoch 423 ------------------------------- Train loss: 0.001612 Epoch 424 ------------------------------- Train loss: 0.001588 Epoch 425 ------------------------------- Train loss: 0.001546 Epoch 426 ------------------------------- Train loss: 0.001575 Epoch 427 ------------------------------- Train loss: 0.001535 Epoch 428 ------------------------------- Train loss: 0.001621 Epoch 429 ------------------------------- Train loss: 0.001639 Epoch 430 ------------------------------- Train loss: 0.001548 Epoch 431 ------------------------------- Train loss: 0.001529 Epoch 432 ------------------------------- Train loss: 0.001555 Epoch 433 ------------------------------- Train loss: 0.001554 Epoch 434 ------------------------------- Train loss: 0.001517 Epoch 435 ------------------------------- Train loss: 0.001463 Epoch 436 ------------------------------- Train loss: 0.001481 Epoch 437 ------------------------------- Train loss: 0.001488 Epoch 438 ------------------------------- Train loss: 0.001482 Epoch 439 ------------------------------- Train loss: 0.001463 Epoch 440 ------------------------------- Train loss: 0.001449 Epoch 441 ------------------------------- Train loss: 0.001451 Epoch 442 ------------------------------- Train loss: 0.001471 Epoch 443 ------------------------------- Train loss: 0.001464 Epoch 444 ------------------------------- Train loss: 0.001498 Epoch 445 ------------------------------- Train loss: 0.001501 Epoch 446 ------------------------------- Train loss: 0.001552 Epoch 447 ------------------------------- Train loss: 0.001541 Epoch 448 ------------------------------- Train loss: 0.001565 Epoch 449 ------------------------------- Train loss: 0.001518 Epoch 450 ------------------------------- Train loss: 0.001498 Epoch 451 ------------------------------- Train loss: 0.001462 Epoch 452 ------------------------------- Train loss: 0.001434 Epoch 453 ------------------------------- Train loss: 0.001443 Epoch 454 ------------------------------- Train loss: 0.001405 Epoch 455 ------------------------------- Train loss: 0.001420 Epoch 456 ------------------------------- Train loss: 0.001391 Epoch 457 ------------------------------- Train loss: 0.001412 Epoch 458 ------------------------------- Train loss: 0.001393 Epoch 459 ------------------------------- Train loss: 0.001396 Epoch 460 ------------------------------- Train loss: 0.001386 Epoch 461 ------------------------------- Train loss: 0.001392 Epoch 462 ------------------------------- Train loss: 0.001390 Epoch 463 ------------------------------- Train loss: 0.001416 Epoch 464 ------------------------------- Train loss: 0.001422 Epoch 465 ------------------------------- Train loss: 0.001476 Epoch 466 ------------------------------- Train loss: 0.001496 Epoch 467 ------------------------------- Train loss: 0.001600 Epoch 468 ------------------------------- Train loss: 0.001557 Epoch 469 ------------------------------- Train loss: 0.001577 Epoch 470 ------------------------------- Train loss: 0.001472 Epoch 471 ------------------------------- Train loss: 0.001387 Epoch 472 ------------------------------- Train loss: 0.001382 Epoch 473 ------------------------------- Train loss: 0.001349 Epoch 474 ------------------------------- Train loss: 0.001395 Epoch 475 ------------------------------- Train loss: 0.001376 Epoch 476 ------------------------------- Train loss: 0.001421 Epoch 477 ------------------------------- Train loss: 0.001392 Epoch 478 ------------------------------- Train loss: 0.001406 Epoch 479 ------------------------------- Train loss: 0.001370 Epoch 480 ------------------------------- Train loss: 0.001362 Epoch 481 ------------------------------- Train loss: 0.001338 Epoch 482 ------------------------------- Train loss: 0.001332 Epoch 483 ------------------------------- Train loss: 0.001324 Epoch 484 ------------------------------- Train loss: 0.001331 Epoch 485 ------------------------------- Train loss: 0.001343 Epoch 486 ------------------------------- Train loss: 0.001360 Epoch 487 ------------------------------- Train loss: 0.001400 Epoch 488 ------------------------------- Train loss: 0.001427 Epoch 489 ------------------------------- Train loss: 0.001521 Epoch 490 ------------------------------- Train loss: 0.001486 Epoch 491 ------------------------------- Train loss: 0.001513 Epoch 492 ------------------------------- Train loss: 0.001409 Epoch 493 ------------------------------- Train loss: 0.001364 Epoch 494 ------------------------------- Train loss: 0.001333 Epoch 495 ------------------------------- Train loss: 0.001316 Epoch 496 ------------------------------- Train loss: 0.001337 Epoch 497 ------------------------------- Train loss: 0.001345 Epoch 498 ------------------------------- Train loss: 0.001389 Epoch 499 ------------------------------- Train loss: 0.001379 Epoch 500 ------------------------------- Train loss: 0.001404 Done!
model.to('cpu')
with torch.no_grad():
Xt, sr=librosa.load('//content/drive/MyDrive/deep learning/Assignment2/train_dirty_male.wav', sr= None)
Xt=librosa.stft(Xt, n_fft=1024, hop_length=512)
Xt_abs = np.abs(Xt)
Xt_abs.astype(np.float32)
Yt_abs = np.asarray(model(torch.tensor(Xt_abs.T))).T
Yt = (Xt/ Xt_abs) * Yt_abs
Yt = librosa.istft( Yt, hop_length=512)
sf.write('Qs1_train_clean_male_recons_v2.wav', Yt, sr)
# ground truth output
s, sr = librosa.load('/content/drive/MyDrive/deep learning/Assignment2/train_clean_male.wav', sr= None)
# predicted output
s_hat, sr_hat = librosa.load('/content/Qs1_train_clean_male_recons_v2.wav', sr= None)
# SNR function based on the assignment formula
def snr(s, s_hat, e=1e-20):
snr = 10 * np.log10((np.sum(np.square(s))) / (np.sum(np.square(np.subtract(s, s_hat))) + e))
return snr
len(s_hat)
1258496
# match the output size to the one with lower length
print(snr(s[:len(s_hat)], s_hat))
14.86742453320684
import soundfile as sf
model.to('cpu')
with torch.no_grad():
Xt1, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/test_x_01.wav', sr= None)
Xt2, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/test_x_02.wav', sr= None)
Xt1=librosa.stft(Xt1, n_fft=1024, hop_length=512)
Xt2=librosa.stft(Xt2, n_fft=1024, hop_length=512)
Xt1_abs = np.abs(Xt1)
Xt2_abs = np.abs(Xt2)
Xt1_abs.astype(np.float32)
Xt2_abs.astype(np.float32)
Yt1_abs = np.asarray(model(torch.tensor(Xt1_abs.T))).T
Yt2_abs = np.asarray(model(torch.tensor(Xt2_abs.T))).T
Yt1 = (Xt1/ Xt1_abs) * Yt1_abs
Yt2 = (Xt2/ Xt2_abs) * Yt2_abs
Yt1 = librosa.istft( Yt1, hop_length=512)
Yt2 = librosa.istft( Yt2, hop_length=512)
sf.write('Qs1_test_s_01_recons.wav', Yt1, sr)
sf.write('Qs1_test_s_02_recons.wav', Yt2, sr)
s, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/train_clean_male.wav', sr=None)
S=librosa.stft(s, n_fft=1024, hop_length=512)
sn, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/train_dirty_male.wav', sr=None)
X=librosa.stft(sn, n_fft=1024, hop_length=512)
batch_size = 2000
X = X.T
S = S.T
s_mag = np.abs(S)
x_mag = np.abs(X)
X = torch.from_numpy(x_mag)
S = torch.from_numpy(s_mag)
train_dataset = TensorDataset(X, S)
dirty_train, dirty_validation = torch.utils.data.random_split(train_dataset, [2000, 459], generator=torch.Generator().manual_seed(42))
train_dataloader = DataLoader(dirty_train, batch_size=batch_size, shuffle=True)
validation_dataloader = DataLoader(dirty_validation, batch_size=batch_size, shuffle=True)
# calculate the output size for different input sizes
input = torch.randn(128, 1, 513)
conv = nn.Conv1d(1, 6, 6, stride=2)
output = conv(input)
print(output.shape)
torch.Size([128, 6, 254])
class CNN_1D(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv1d(in_channels=1, out_channels=6, kernel_size=6, stride=2)
self.fc1 = nn.Linear(6*254, 513)
def forward(self, x):
x = x.unsqueeze(1)
x = F.relu(self.conv1(x))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
return x
cnn_1d = CNN_1D()
# cnn_1d = cnn_1d.to(device)
loss_fn = torch.nn.MSELoss()
optimizer = torch.optim.Adam(cnn_1d.parameters())
cnn_1d.apply(xavier_initializer)
scheduler = StepLR(optimizer, step_size=50, gamma=0.1)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_. This is separate from the ipykernel package so we can avoid doing imports until
train_epochs(train_dataloader, cnn_1d, loss_fn, optimizer)
Epoch 1 ------------------------------- Train loss: 0.130368 Epoch 2 ------------------------------- Train loss: 0.097214 Epoch 3 ------------------------------- Train loss: 0.088212 Epoch 4 ------------------------------- Train loss: 0.086008 Epoch 5 ------------------------------- Train loss: 0.084805 Epoch 6 ------------------------------- Train loss: 0.083504 Epoch 7 ------------------------------- Train loss: 0.082068 Epoch 8 ------------------------------- Train loss: 0.080553 Epoch 9 ------------------------------- Train loss: 0.078963 Epoch 10 ------------------------------- Train loss: 0.077731 Epoch 11 ------------------------------- Train loss: 0.076414 Epoch 12 ------------------------------- Train loss: 0.074957 Epoch 13 ------------------------------- Train loss: 0.073510 Epoch 14 ------------------------------- Train loss: 0.072127 Epoch 15 ------------------------------- Train loss: 0.070837 Epoch 16 ------------------------------- Train loss: 0.069589 Epoch 17 ------------------------------- Train loss: 0.068297 Epoch 18 ------------------------------- Train loss: 0.066991 Epoch 19 ------------------------------- Train loss: 0.065774 Epoch 20 ------------------------------- Train loss: 0.064597 Epoch 21 ------------------------------- Train loss: 0.063435 Epoch 22 ------------------------------- Train loss: 0.062321 Epoch 23 ------------------------------- Train loss: 0.061282 Epoch 24 ------------------------------- Train loss: 0.060236 Epoch 25 ------------------------------- Train loss: 0.059234 Epoch 26 ------------------------------- Train loss: 0.058270 Epoch 27 ------------------------------- Train loss: 0.057308 Epoch 28 ------------------------------- Train loss: 0.056346 Epoch 29 ------------------------------- Train loss: 0.055414 Epoch 30 ------------------------------- Train loss: 0.054509 Epoch 31 ------------------------------- Train loss: 0.053630 Epoch 32 ------------------------------- Train loss: 0.052773 Epoch 33 ------------------------------- Train loss: 0.051950 Epoch 34 ------------------------------- Train loss: 0.051157 Epoch 35 ------------------------------- Train loss: 0.050338 Epoch 36 ------------------------------- Train loss: 0.049524 Epoch 37 ------------------------------- Train loss: 0.048733 Epoch 38 ------------------------------- Train loss: 0.047968 Epoch 39 ------------------------------- Train loss: 0.047212 Epoch 40 ------------------------------- Train loss: 0.046481 Epoch 41 ------------------------------- Train loss: 0.045737 Epoch 42 ------------------------------- Train loss: 0.045011 Epoch 43 ------------------------------- Train loss: 0.044271 Epoch 44 ------------------------------- Train loss: 0.043551 Epoch 45 ------------------------------- Train loss: 0.042858 Epoch 46 ------------------------------- Train loss: 0.042174 Epoch 47 ------------------------------- Train loss: 0.041555 Epoch 48 ------------------------------- Train loss: 0.040905 Epoch 49 ------------------------------- Train loss: 0.040249 Epoch 50 ------------------------------- Train loss: 0.039596 Epoch 51 ------------------------------- Train loss: 0.038986 Epoch 52 ------------------------------- Train loss: 0.038390 Epoch 53 ------------------------------- Train loss: 0.037799 Epoch 54 ------------------------------- Train loss: 0.037215 Epoch 55 ------------------------------- Train loss: 0.036634 Epoch 56 ------------------------------- Train loss: 0.036065 Epoch 57 ------------------------------- Train loss: 0.035507 Epoch 58 ------------------------------- Train loss: 0.034958 Epoch 59 ------------------------------- Train loss: 0.034428 Epoch 60 ------------------------------- Train loss: 0.033909 Epoch 61 ------------------------------- Train loss: 0.033447 Epoch 62 ------------------------------- Train loss: 0.032889 Epoch 63 ------------------------------- Train loss: 0.032399 Epoch 64 ------------------------------- Train loss: 0.031919 Epoch 65 ------------------------------- Train loss: 0.031454 Epoch 66 ------------------------------- Train loss: 0.030991 Epoch 67 ------------------------------- Train loss: 0.030541 Epoch 68 ------------------------------- Train loss: 0.030097 Epoch 69 ------------------------------- Train loss: 0.029665 Epoch 70 ------------------------------- Train loss: 0.029244 Epoch 71 ------------------------------- Train loss: 0.028835 Epoch 72 ------------------------------- Train loss: 0.028436 Epoch 73 ------------------------------- Train loss: 0.028043 Epoch 74 ------------------------------- Train loss: 0.027661 Epoch 75 ------------------------------- Train loss: 0.027292 Epoch 76 ------------------------------- Train loss: 0.026931 Epoch 77 ------------------------------- Train loss: 0.026577 Epoch 78 ------------------------------- Train loss: 0.026232 Epoch 79 ------------------------------- Train loss: 0.025897 Epoch 80 ------------------------------- Train loss: 0.025572 Epoch 81 ------------------------------- Train loss: 0.025256 Epoch 82 ------------------------------- Train loss: 0.024945 Epoch 83 ------------------------------- Train loss: 0.024643 Epoch 84 ------------------------------- Train loss: 0.024353 Epoch 85 ------------------------------- Train loss: 0.024062 Epoch 86 ------------------------------- Train loss: 0.023784 Epoch 87 ------------------------------- Train loss: 0.023512 Epoch 88 ------------------------------- Train loss: 0.023248 Epoch 89 ------------------------------- Train loss: 0.022988 Epoch 90 ------------------------------- Train loss: 0.022733 Epoch 91 ------------------------------- Train loss: 0.022486 Epoch 92 ------------------------------- Train loss: 0.022245 Epoch 93 ------------------------------- Train loss: 0.022012 Epoch 94 ------------------------------- Train loss: 0.021781 Epoch 95 ------------------------------- Train loss: 0.021558 Epoch 96 ------------------------------- Train loss: 0.021340 Epoch 97 ------------------------------- Train loss: 0.021128 Epoch 98 ------------------------------- Train loss: 0.020921 Epoch 99 ------------------------------- Train loss: 0.020716 Epoch 100 ------------------------------- Train loss: 0.020527 Epoch 101 ------------------------------- Train loss: 0.020330 Epoch 102 ------------------------------- Train loss: 0.020152 Epoch 103 ------------------------------- Train loss: 0.019968 Epoch 104 ------------------------------- Train loss: 0.019789 Epoch 105 ------------------------------- Train loss: 0.019611 Epoch 106 ------------------------------- Train loss: 0.019432 Epoch 107 ------------------------------- Train loss: 0.019281 Epoch 108 ------------------------------- Train loss: 0.019104 Epoch 109 ------------------------------- Train loss: 0.018948 Epoch 110 ------------------------------- Train loss: 0.018790 Epoch 111 ------------------------------- Train loss: 0.018631 Epoch 112 ------------------------------- Train loss: 0.018477 Epoch 113 ------------------------------- Train loss: 0.018332 Epoch 114 ------------------------------- Train loss: 0.018184 Epoch 115 ------------------------------- Train loss: 0.018041 Epoch 116 ------------------------------- Train loss: 0.017906 Epoch 117 ------------------------------- Train loss: 0.017767 Epoch 118 ------------------------------- Train loss: 0.017632 Epoch 119 ------------------------------- Train loss: 0.017507 Epoch 120 ------------------------------- Train loss: 0.017377 Epoch 121 ------------------------------- Train loss: 0.017255 Epoch 122 ------------------------------- Train loss: 0.017137 Epoch 123 ------------------------------- Train loss: 0.017016 Epoch 124 ------------------------------- Train loss: 0.016893 Epoch 125 ------------------------------- Train loss: 0.016784 Epoch 126 ------------------------------- Train loss: 0.016666 Epoch 127 ------------------------------- Train loss: 0.016559 Epoch 128 ------------------------------- Train loss: 0.016452 Epoch 129 ------------------------------- Train loss: 0.016340 Epoch 130 ------------------------------- Train loss: 0.016241 Epoch 131 ------------------------------- Train loss: 0.016140 Epoch 132 ------------------------------- Train loss: 0.016037 Epoch 133 ------------------------------- Train loss: 0.015944 Epoch 134 ------------------------------- Train loss: 0.015845 Epoch 135 ------------------------------- Train loss: 0.015752 Epoch 136 ------------------------------- Train loss: 0.015659 Epoch 137 ------------------------------- Train loss: 0.015567 Epoch 138 ------------------------------- Train loss: 0.015480 Epoch 139 ------------------------------- Train loss: 0.015393 Epoch 140 ------------------------------- Train loss: 0.015308 Epoch 141 ------------------------------- Train loss: 0.015226 Epoch 142 ------------------------------- Train loss: 0.015143 Epoch 143 ------------------------------- Train loss: 0.015065 Epoch 144 ------------------------------- Train loss: 0.014990 Epoch 145 ------------------------------- Train loss: 0.014908 Epoch 146 ------------------------------- Train loss: 0.014838 Epoch 147 ------------------------------- Train loss: 0.014768 Epoch 148 ------------------------------- Train loss: 0.014697 Epoch 149 ------------------------------- Train loss: 0.014618 Epoch 150 ------------------------------- Train loss: 0.014560 Epoch 151 ------------------------------- Train loss: 0.014483 Epoch 152 ------------------------------- Train loss: 0.014421 Epoch 153 ------------------------------- Train loss: 0.014354 Epoch 154 ------------------------------- Train loss: 0.014284 Epoch 155 ------------------------------- Train loss: 0.014216 Epoch 156 ------------------------------- Train loss: 0.014158 Epoch 157 ------------------------------- Train loss: 0.014091 Epoch 158 ------------------------------- Train loss: 0.014030 Epoch 159 ------------------------------- Train loss: 0.013973 Epoch 160 ------------------------------- Train loss: 0.013913 Epoch 161 ------------------------------- Train loss: 0.013850 Epoch 162 ------------------------------- Train loss: 0.013794 Epoch 163 ------------------------------- Train loss: 0.013741 Epoch 164 ------------------------------- Train loss: 0.013684 Epoch 165 ------------------------------- Train loss: 0.013639 Epoch 166 ------------------------------- Train loss: 0.013582 Epoch 167 ------------------------------- Train loss: 0.013536 Epoch 168 ------------------------------- Train loss: 0.013489 Epoch 169 ------------------------------- Train loss: 0.013437 Epoch 170 ------------------------------- Train loss: 0.013374 Epoch 171 ------------------------------- Train loss: 0.013349 Epoch 172 ------------------------------- Train loss: 0.013282 Epoch 173 ------------------------------- Train loss: 0.013252 Epoch 174 ------------------------------- Train loss: 0.013208 Epoch 175 ------------------------------- Train loss: 0.013159 Epoch 176 ------------------------------- Train loss: 0.013101 Epoch 177 ------------------------------- Train loss: 0.013044 Epoch 178 ------------------------------- Train loss: 0.013025 Epoch 179 ------------------------------- Train loss: 0.012969 Epoch 180 ------------------------------- Train loss: 0.012928 Epoch 181 ------------------------------- Train loss: 0.012890 Epoch 182 ------------------------------- Train loss: 0.012853 Epoch 183 ------------------------------- Train loss: 0.012819 Epoch 184 ------------------------------- Train loss: 0.012766 Epoch 185 ------------------------------- Train loss: 0.012730 Epoch 186 ------------------------------- Train loss: 0.012698 Epoch 187 ------------------------------- Train loss: 0.012647 Epoch 188 ------------------------------- Train loss: 0.012616 Epoch 189 ------------------------------- Train loss: 0.012576 Epoch 190 ------------------------------- Train loss: 0.012539 Epoch 191 ------------------------------- Train loss: 0.012498 Epoch 192 ------------------------------- Train loss: 0.012470 Epoch 193 ------------------------------- Train loss: 0.012424 Epoch 194 ------------------------------- Train loss: 0.012393 Epoch 195 ------------------------------- Train loss: 0.012361 Epoch 196 ------------------------------- Train loss: 0.012325 Epoch 197 ------------------------------- Train loss: 0.012288 Epoch 198 ------------------------------- Train loss: 0.012268 Epoch 199 ------------------------------- Train loss: 0.012229 Epoch 200 ------------------------------- Train loss: 0.012199 Epoch 201 ------------------------------- Train loss: 0.012209 Epoch 202 ------------------------------- Train loss: 0.012151 Epoch 203 ------------------------------- Train loss: 0.012130 Epoch 204 ------------------------------- Train loss: 0.012099 Epoch 205 ------------------------------- Train loss: 0.012063 Epoch 206 ------------------------------- Train loss: 0.012031 Epoch 207 ------------------------------- Train loss: 0.012018 Epoch 208 ------------------------------- Train loss: 0.011979 Epoch 209 ------------------------------- Train loss: 0.011962 Epoch 210 ------------------------------- Train loss: 0.011944 Epoch 211 ------------------------------- Train loss: 0.011923 Epoch 212 ------------------------------- Train loss: 0.011895 Epoch 213 ------------------------------- Train loss: 0.011866 Epoch 214 ------------------------------- Train loss: 0.011845 Epoch 215 ------------------------------- Train loss: 0.011805 Epoch 216 ------------------------------- Train loss: 0.011773 Epoch 217 ------------------------------- Train loss: 0.011772 Epoch 218 ------------------------------- Train loss: 0.011730 Epoch 219 ------------------------------- Train loss: 0.011713 Epoch 220 ------------------------------- Train loss: 0.011691 Epoch 221 ------------------------------- Train loss: 0.011675 Epoch 222 ------------------------------- Train loss: 0.011644 Epoch 223 ------------------------------- Train loss: 0.011620 Epoch 224 ------------------------------- Train loss: 0.011609 Epoch 225 ------------------------------- Train loss: 0.011569 Epoch 226 ------------------------------- Train loss: 0.011552 Epoch 227 ------------------------------- Train loss: 0.011533 Epoch 228 ------------------------------- Train loss: 0.011515 Epoch 229 ------------------------------- Train loss: 0.011489 Epoch 230 ------------------------------- Train loss: 0.011468 Epoch 231 ------------------------------- Train loss: 0.011443 Epoch 232 ------------------------------- Train loss: 0.011432 Epoch 233 ------------------------------- Train loss: 0.011400 Epoch 234 ------------------------------- Train loss: 0.011382 Epoch 235 ------------------------------- Train loss: 0.011369 Epoch 236 ------------------------------- Train loss: 0.011345 Epoch 237 ------------------------------- Train loss: 0.011326 Epoch 238 ------------------------------- Train loss: 0.011306 Epoch 239 ------------------------------- Train loss: 0.011291 Epoch 240 ------------------------------- Train loss: 0.011271 Epoch 241 ------------------------------- Train loss: 0.011252 Epoch 242 ------------------------------- Train loss: 0.011240 Epoch 243 ------------------------------- Train loss: 0.011227 Epoch 244 ------------------------------- Train loss: 0.011215 Epoch 245 ------------------------------- Train loss: 0.011201 Epoch 246 ------------------------------- Train loss: 0.011184 Epoch 247 ------------------------------- Train loss: 0.011162 Epoch 248 ------------------------------- Train loss: 0.011145 Epoch 249 ------------------------------- Train loss: 0.011122 Epoch 250 ------------------------------- Train loss: 0.011108 Epoch 251 ------------------------------- Train loss: 0.011096 Epoch 252 ------------------------------- Train loss: 0.011072 Epoch 253 ------------------------------- Train loss: 0.011054 Epoch 254 ------------------------------- Train loss: 0.011045 Epoch 255 ------------------------------- Train loss: 0.011020 Epoch 256 ------------------------------- Train loss: 0.011010 Epoch 257 ------------------------------- Train loss: 0.011001 Epoch 258 ------------------------------- Train loss: 0.010982 Epoch 259 ------------------------------- Train loss: 0.010970 Epoch 260 ------------------------------- Train loss: 0.010961 Epoch 261 ------------------------------- Train loss: 0.010945 Epoch 262 ------------------------------- Train loss: 0.010938 Epoch 263 ------------------------------- Train loss: 0.010916 Epoch 264 ------------------------------- Train loss: 0.010897 Epoch 265 ------------------------------- Train loss: 0.010893 Epoch 266 ------------------------------- Train loss: 0.010881 Epoch 267 ------------------------------- Train loss: 0.010882 Epoch 268 ------------------------------- Train loss: 0.010869 Epoch 269 ------------------------------- Train loss: 0.010849 Epoch 270 ------------------------------- Train loss: 0.010823 Epoch 271 ------------------------------- Train loss: 0.010835 Epoch 272 ------------------------------- Train loss: 0.010798 Epoch 273 ------------------------------- Train loss: 0.010796 Epoch 274 ------------------------------- Train loss: 0.010790 Epoch 275 ------------------------------- Train loss: 0.010779 Epoch 276 ------------------------------- Train loss: 0.010763 Epoch 277 ------------------------------- Train loss: 0.010744 Epoch 278 ------------------------------- Train loss: 0.010748 Epoch 279 ------------------------------- Train loss: 0.010720 Epoch 280 ------------------------------- Train loss: 0.010717 Epoch 281 ------------------------------- Train loss: 0.010713 Epoch 282 ------------------------------- Train loss: 0.010702 Epoch 283 ------------------------------- Train loss: 0.010688 Epoch 284 ------------------------------- Train loss: 0.010672 Epoch 285 ------------------------------- Train loss: 0.010664 Epoch 286 ------------------------------- Train loss: 0.010647 Epoch 287 ------------------------------- Train loss: 0.010640 Epoch 288 ------------------------------- Train loss: 0.010631 Epoch 289 ------------------------------- Train loss: 0.010616 Epoch 290 ------------------------------- Train loss: 0.010605 Epoch 291 ------------------------------- Train loss: 0.010599 Epoch 292 ------------------------------- Train loss: 0.010581 Epoch 293 ------------------------------- Train loss: 0.010585 Epoch 294 ------------------------------- Train loss: 0.010566 Epoch 295 ------------------------------- Train loss: 0.010564 Epoch 296 ------------------------------- Train loss: 0.010549 Epoch 297 ------------------------------- Train loss: 0.010541 Epoch 298 ------------------------------- Train loss: 0.010528 Epoch 299 ------------------------------- Train loss: 0.010523 Epoch 300 ------------------------------- Train loss: 0.010509 Epoch 301 ------------------------------- Train loss: 0.010502 Epoch 302 ------------------------------- Train loss: 0.010494 Epoch 303 ------------------------------- Train loss: 0.010485 Epoch 304 ------------------------------- Train loss: 0.010473 Epoch 305 ------------------------------- Train loss: 0.010470 Epoch 306 ------------------------------- Train loss: 0.010458 Epoch 307 ------------------------------- Train loss: 0.010446 Epoch 308 ------------------------------- Train loss: 0.010439 Epoch 309 ------------------------------- Train loss: 0.010430 Epoch 310 ------------------------------- Train loss: 0.010423 Epoch 311 ------------------------------- Train loss: 0.010415 Epoch 312 ------------------------------- Train loss: 0.010407 Epoch 313 ------------------------------- Train loss: 0.010399 Epoch 314 ------------------------------- Train loss: 0.010390 Epoch 315 ------------------------------- Train loss: 0.010384 Epoch 316 ------------------------------- Train loss: 0.010372 Epoch 317 ------------------------------- Train loss: 0.010366 Epoch 318 ------------------------------- Train loss: 0.010357 Epoch 319 ------------------------------- Train loss: 0.010349 Epoch 320 ------------------------------- Train loss: 0.010343 Epoch 321 ------------------------------- Train loss: 0.010333 Epoch 322 ------------------------------- Train loss: 0.010326 Epoch 323 ------------------------------- Train loss: 0.010319 Epoch 324 ------------------------------- Train loss: 0.010311 Epoch 325 ------------------------------- Train loss: 0.010307 Epoch 326 ------------------------------- Train loss: 0.010298 Epoch 327 ------------------------------- Train loss: 0.010292 Epoch 328 ------------------------------- Train loss: 0.010286 Epoch 329 ------------------------------- Train loss: 0.010278 Epoch 330 ------------------------------- Train loss: 0.010273 Epoch 331 ------------------------------- Train loss: 0.010266 Epoch 332 ------------------------------- Train loss: 0.010262 Epoch 333 ------------------------------- Train loss: 0.010253 Epoch 334 ------------------------------- Train loss: 0.010246 Epoch 335 ------------------------------- Train loss: 0.010239 Epoch 336 ------------------------------- Train loss: 0.010231 Epoch 337 ------------------------------- Train loss: 0.010227 Epoch 338 ------------------------------- Train loss: 0.010224 Epoch 339 ------------------------------- Train loss: 0.010221 Epoch 340 ------------------------------- Train loss: 0.010211 Epoch 341 ------------------------------- Train loss: 0.010208 Epoch 342 ------------------------------- Train loss: 0.010199 Epoch 343 ------------------------------- Train loss: 0.010196 Epoch 344 ------------------------------- Train loss: 0.010188 Epoch 345 ------------------------------- Train loss: 0.010179 Epoch 346 ------------------------------- Train loss: 0.010180 Epoch 347 ------------------------------- Train loss: 0.010171 Epoch 348 ------------------------------- Train loss: 0.010170 Epoch 349 ------------------------------- Train loss: 0.010165 Epoch 350 ------------------------------- Train loss: 0.010158 Epoch 351 ------------------------------- Train loss: 0.010150 Epoch 352 ------------------------------- Train loss: 0.010141 Epoch 353 ------------------------------- Train loss: 0.010144 Epoch 354 ------------------------------- Train loss: 0.010135 Epoch 355 ------------------------------- Train loss: 0.010132 Epoch 356 ------------------------------- Train loss: 0.010136 Epoch 357 ------------------------------- Train loss: 0.010174 Epoch 358 ------------------------------- Train loss: 0.010135 Epoch 359 ------------------------------- Train loss: 0.010131 Epoch 360 ------------------------------- Train loss: 0.010124 Epoch 361 ------------------------------- Train loss: 0.010117 Epoch 362 ------------------------------- Train loss: 0.010106 Epoch 363 ------------------------------- Train loss: 0.010098 Epoch 364 ------------------------------- Train loss: 0.010093 Epoch 365 ------------------------------- Train loss: 0.010080 Epoch 366 ------------------------------- Train loss: 0.010083 Epoch 367 ------------------------------- Train loss: 0.010069 Epoch 368 ------------------------------- Train loss: 0.010065 Epoch 369 ------------------------------- Train loss: 0.010061 Epoch 370 ------------------------------- Train loss: 0.010054 Epoch 371 ------------------------------- Train loss: 0.010045 Epoch 372 ------------------------------- Train loss: 0.010048 Epoch 373 ------------------------------- Train loss: 0.010035 Epoch 374 ------------------------------- Train loss: 0.010033 Epoch 375 ------------------------------- Train loss: 0.010029 Epoch 376 ------------------------------- Train loss: 0.010022 Epoch 377 ------------------------------- Train loss: 0.010014 Epoch 378 ------------------------------- Train loss: 0.010011 Epoch 379 ------------------------------- Train loss: 0.010005 Epoch 380 ------------------------------- Train loss: 0.010004 Epoch 381 ------------------------------- Train loss: 0.009999 Epoch 382 ------------------------------- Train loss: 0.009993 Epoch 383 ------------------------------- Train loss: 0.009992 Epoch 384 ------------------------------- Train loss: 0.009984 Epoch 385 ------------------------------- Train loss: 0.009980 Epoch 386 ------------------------------- Train loss: 0.009976 Epoch 387 ------------------------------- Train loss: 0.009970 Epoch 388 ------------------------------- Train loss: 0.009964 Epoch 389 ------------------------------- Train loss: 0.009961 Epoch 390 ------------------------------- Train loss: 0.009957 Epoch 391 ------------------------------- Train loss: 0.009955 Epoch 392 ------------------------------- Train loss: 0.009949 Epoch 393 ------------------------------- Train loss: 0.009945 Epoch 394 ------------------------------- Train loss: 0.009938 Epoch 395 ------------------------------- Train loss: 0.009938 Epoch 396 ------------------------------- Train loss: 0.009930 Epoch 397 ------------------------------- Train loss: 0.009932 Epoch 398 ------------------------------- Train loss: 0.009933 Epoch 399 ------------------------------- Train loss: 0.009936 Epoch 400 ------------------------------- Train loss: 0.009935 Epoch 401 ------------------------------- Train loss: 0.009930 Epoch 402 ------------------------------- Train loss: 0.009926 Epoch 403 ------------------------------- Train loss: 0.009916 Epoch 404 ------------------------------- Train loss: 0.009906 Epoch 405 ------------------------------- Train loss: 0.009909 Epoch 406 ------------------------------- Train loss: 0.009896 Epoch 407 ------------------------------- Train loss: 0.009894 Epoch 408 ------------------------------- Train loss: 0.009893 Epoch 409 ------------------------------- Train loss: 0.009892 Epoch 410 ------------------------------- Train loss: 0.009891 Epoch 411 ------------------------------- Train loss: 0.009894 Epoch 412 ------------------------------- Train loss: 0.009889 Epoch 413 ------------------------------- Train loss: 0.009883 Epoch 414 ------------------------------- Train loss: 0.009878 Epoch 415 ------------------------------- Train loss: 0.009867 Epoch 416 ------------------------------- Train loss: 0.009864 Epoch 417 ------------------------------- Train loss: 0.009855 Epoch 418 ------------------------------- Train loss: 0.009863 Epoch 419 ------------------------------- Train loss: 0.009850 Epoch 420 ------------------------------- Train loss: 0.009850 Epoch 421 ------------------------------- Train loss: 0.009851 Epoch 422 ------------------------------- Train loss: 0.009848 Epoch 423 ------------------------------- Train loss: 0.009846 Epoch 424 ------------------------------- Train loss: 0.009840 Epoch 425 ------------------------------- Train loss: 0.009834 Epoch 426 ------------------------------- Train loss: 0.009826 Epoch 427 ------------------------------- Train loss: 0.009823 Epoch 428 ------------------------------- Train loss: 0.009822 Epoch 429 ------------------------------- Train loss: 0.009818 Epoch 430 ------------------------------- Train loss: 0.009818 Epoch 431 ------------------------------- Train loss: 0.009819 Epoch 432 ------------------------------- Train loss: 0.009812 Epoch 433 ------------------------------- Train loss: 0.009807 Epoch 434 ------------------------------- Train loss: 0.009801 Epoch 435 ------------------------------- Train loss: 0.009796 Epoch 436 ------------------------------- Train loss: 0.009796 Epoch 437 ------------------------------- Train loss: 0.009790 Epoch 438 ------------------------------- Train loss: 0.009786 Epoch 439 ------------------------------- Train loss: 0.009785 Epoch 440 ------------------------------- Train loss: 0.009782 Epoch 441 ------------------------------- Train loss: 0.009777 Epoch 442 ------------------------------- Train loss: 0.009776 Epoch 443 ------------------------------- Train loss: 0.009769 Epoch 444 ------------------------------- Train loss: 0.009768 Epoch 445 ------------------------------- Train loss: 0.009765 Epoch 446 ------------------------------- Train loss: 0.009760 Epoch 447 ------------------------------- Train loss: 0.009759 Epoch 448 ------------------------------- Train loss: 0.009758 Epoch 449 ------------------------------- Train loss: 0.009757 Epoch 450 ------------------------------- Train loss: 0.009751 Epoch 451 ------------------------------- Train loss: 0.009744 Epoch 452 ------------------------------- Train loss: 0.009754 Epoch 453 ------------------------------- Train loss: 0.009745 Epoch 454 ------------------------------- Train loss: 0.009747 Epoch 455 ------------------------------- Train loss: 0.009745 Epoch 456 ------------------------------- Train loss: 0.009744 Epoch 457 ------------------------------- Train loss: 0.009742 Epoch 458 ------------------------------- Train loss: 0.009739 Epoch 459 ------------------------------- Train loss: 0.009738 Epoch 460 ------------------------------- Train loss: 0.009734 Epoch 461 ------------------------------- Train loss: 0.009732 Epoch 462 ------------------------------- Train loss: 0.009730 Epoch 463 ------------------------------- Train loss: 0.009724 Epoch 464 ------------------------------- Train loss: 0.009721 Epoch 465 ------------------------------- Train loss: 0.009718 Epoch 466 ------------------------------- Train loss: 0.009716 Epoch 467 ------------------------------- Train loss: 0.009715 Epoch 468 ------------------------------- Train loss: 0.009712 Epoch 469 ------------------------------- Train loss: 0.009711 Epoch 470 ------------------------------- Train loss: 0.009709 Epoch 471 ------------------------------- Train loss: 0.009704 Epoch 472 ------------------------------- Train loss: 0.009703 Epoch 473 ------------------------------- Train loss: 0.009698 Epoch 474 ------------------------------- Train loss: 0.009697 Epoch 475 ------------------------------- Train loss: 0.009695 Epoch 476 ------------------------------- Train loss: 0.009694 Epoch 477 ------------------------------- Train loss: 0.009691 Epoch 478 ------------------------------- Train loss: 0.009689 Epoch 479 ------------------------------- Train loss: 0.009685 Epoch 480 ------------------------------- Train loss: 0.009682 Epoch 481 ------------------------------- Train loss: 0.009679 Epoch 482 ------------------------------- Train loss: 0.009678 Epoch 483 ------------------------------- Train loss: 0.009678 Epoch 484 ------------------------------- Train loss: 0.009675 Epoch 485 ------------------------------- Train loss: 0.009674 Epoch 486 ------------------------------- Train loss: 0.009672 Epoch 487 ------------------------------- Train loss: 0.009668 Epoch 488 ------------------------------- Train loss: 0.009664 Epoch 489 ------------------------------- Train loss: 0.009667 Epoch 490 ------------------------------- Train loss: 0.009667 Epoch 491 ------------------------------- Train loss: 0.009672 Epoch 492 ------------------------------- Train loss: 0.009673 Epoch 493 ------------------------------- Train loss: 0.009671 Epoch 494 ------------------------------- Train loss: 0.009664 Epoch 495 ------------------------------- Train loss: 0.009659 Epoch 496 ------------------------------- Train loss: 0.009658 Epoch 497 ------------------------------- Train loss: 0.009659 Epoch 498 ------------------------------- Train loss: 0.009650 Epoch 499 ------------------------------- Train loss: 0.009649 Epoch 500 ------------------------------- Train loss: 0.009649 Done!
import soundfile as sf
cnn_1d.to('cpu')
with torch.no_grad():
Xt, sr=librosa.load('//content/drive/MyDrive/deep learning/Assignment2/train_dirty_male.wav', sr= None)
Xt=librosa.stft(Xt, n_fft=1024, hop_length=512)
Xt_abs = np.abs(Xt)
Xt_abs.astype(np.float32)
Yt_abs = np.asarray(cnn_1d(torch.tensor(Xt_abs.T))).T
Yt = (Xt/ Xt_abs) * Yt_abs
Yt = librosa.istft( Yt, hop_length=512)
sf.write('Qs2_train_clean_male_recons_cnn1D.wav', Yt, sr)
s, sr = librosa.load('/content/drive/MyDrive/deep learning/Assignment2/train_clean_male.wav', sr= None)
s_hat, sr_hat = librosa.load('/content/Qs2_train_clean_male_recons_cnn1D.wav', sr= None)
print(len(s),',', len(s_hat))
1258899 , 1258496
print(snr(s[:len(s_hat)], s_hat))
10.768744137624973
import soundfile as sf
model.to('cpu')
with torch.no_grad():
Xt1, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/test_x_01.wav', sr= None)
Xt2, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/test_x_02.wav', sr= None)
Xt1=librosa.stft(Xt1, n_fft=1024, hop_length=512)
Xt2=librosa.stft(Xt2, n_fft=1024, hop_length=512)
Xt1_abs = np.abs(Xt1)
Xt2_abs = np.abs(Xt2)
Xt1_abs.astype(np.float32)
Xt2_abs.astype(np.float32)
Yt1_abs = np.asarray(cnn_1d(torch.tensor(Xt1_abs.T))).T
Yt2_abs = np.asarray(cnn_1d(torch.tensor(Xt2_abs.T))).T
Yt1 = (Xt1/ Xt1_abs) * Yt1_abs
Yt2 = (Xt2/ Xt2_abs) * Yt2_abs
Yt1 = librosa.istft( Yt1, hop_length=512)
Yt2 = librosa.istft( Yt2, hop_length=512)
sf.write('Qs2_test_s_01_recons_cnn1D.wav', Yt1, sr)
sf.write('Qs2_test_s_02_recons_cnn1D.wav', Yt2, sr)
s, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/train_clean_male.wav', sr=None)
S=librosa.stft(s, n_fft=1024, hop_length=512)
sn, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/train_dirty_male.wav', sr=None)
X=librosa.stft(sn, n_fft=1024, hop_length=512)
print(S.shape, X.shape)
(513, 2459) (513, 2459)
def convert_voice_to_img(input):
imgs = []
# because we're getting 2 dimenensional data
for i in range(input.shape[0]-19):
imgs.append(input[i:i+20])
# print(imgs)
return imgs
batch_size = 128
X = X.T
S = S.T
# print(S.shape, X.shape)
#get images as input
X = convert_voice_to_img(X)
S = S[:-19]
# print(S.shape, len(X))
s_mag = np.abs(S)
x_mag = np.abs(X)
X = torch.from_numpy(x_mag)
S = torch.from_numpy(s_mag)
train_dataset = TensorDataset(X, S)
dirty_train, dirty_validation = torch.utils.data.random_split(train_dataset, [2000, 440], generator=torch.Generator().manual_seed(42))
train_dataloader = DataLoader(dirty_train, batch_size=batch_size, shuffle=True)
# calculate the output size for different input sizes
input = torch.randn(128, 5, 8, 255)
conv = nn.Conv2d(5, 12, 6)
output = conv(input)
print(output.shape)
torch.Size([128, 12, 3, 250])
class CNN_2D(nn.Module):
def __init__(self):
super(CNN_2D, self).__init__()
self.conv1 = nn.Conv2d(1, 5, 7, stride=2, padding=1)
self.conv2 = nn.Conv2d(5, 12, 6)
self.fc1 = nn.Linear(12 * 3 * 250, 2048)
self.fc2 = nn.Linear(2048, 2048)
self.fc3 = nn.Linear(2048, 513)
def forward(self, x):
x = x.unsqueeze(1)
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
return x
cnn_2d = CNN_2D()
cnn_2d.to(device)
loss_fn = torch.nn.MSELoss()
optimizer = torch.optim.Adam(cnn_2d.parameters())
cnn_2d.apply(xavier_initializer)
scheduler = StepLR(optimizer, step_size=50, gamma=0.1)
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: nn.init.xavier_normal is now deprecated in favor of nn.init.xavier_normal_. This is separate from the ipykernel package so we can avoid doing imports until
train_epochs(train_dataloader, cnn_2d, loss_fn, optimizer)
Epoch 1 ------------------------------- Train loss: 0.105946 Train loss: 0.088635 Epoch 2 ------------------------------- Train loss: 0.087603 Train loss: 0.042609 Epoch 3 ------------------------------- Train loss: 0.087446 Train loss: 0.059393 Epoch 4 ------------------------------- Train loss: 0.042302 Train loss: 0.086550 Epoch 5 ------------------------------- Train loss: 0.039375 Train loss: 0.071180 Epoch 6 ------------------------------- Train loss: 0.041910 Train loss: 0.023024 Epoch 7 ------------------------------- Train loss: 0.070332 Train loss: 0.053047 Epoch 8 ------------------------------- Train loss: 0.038920 Train loss: 0.024555 Epoch 9 ------------------------------- Train loss: 0.022816 Train loss: 0.023596 Epoch 10 ------------------------------- Train loss: 0.037217 Train loss: 0.034189 Epoch 11 ------------------------------- Train loss: 0.021788 Train loss: 0.023155 Epoch 12 ------------------------------- Train loss: 0.018902 Train loss: 0.026699 Epoch 13 ------------------------------- Train loss: 0.019752 Train loss: 0.018650 Epoch 14 ------------------------------- Train loss: 0.012019 Train loss: 0.016812 Epoch 15 ------------------------------- Train loss: 0.012610 Train loss: 0.019311 Epoch 16 ------------------------------- Train loss: 0.011235 Train loss: 0.014675 Epoch 17 ------------------------------- Train loss: 0.014879 Train loss: 0.012107 Epoch 18 ------------------------------- Train loss: 0.008493 Train loss: 0.010130 Epoch 19 ------------------------------- Train loss: 0.009302 Train loss: 0.015380 Epoch 20 ------------------------------- Train loss: 0.010791 Train loss: 0.010975 Epoch 21 ------------------------------- Train loss: 0.011382 Train loss: 0.011491 Epoch 22 ------------------------------- Train loss: 0.009596 Train loss: 0.009097 Epoch 23 ------------------------------- Train loss: 0.007652 Train loss: 0.008513 Epoch 24 ------------------------------- Train loss: 0.006657 Train loss: 0.007841 Epoch 25 ------------------------------- Train loss: 0.007430 Train loss: 0.006057 Epoch 26 ------------------------------- Train loss: 0.007930 Train loss: 0.007815 Epoch 27 ------------------------------- Train loss: 0.005434 Train loss: 0.004968 Epoch 28 ------------------------------- Train loss: 0.003944 Train loss: 0.004341 Epoch 29 ------------------------------- Train loss: 0.006318 Train loss: 0.004831 Epoch 30 ------------------------------- Train loss: 0.006060 Train loss: 0.006473 Epoch 31 ------------------------------- Train loss: 0.004711 Train loss: 0.004949 Epoch 32 ------------------------------- Train loss: 0.004504 Train loss: 0.004404 Epoch 33 ------------------------------- Train loss: 0.006011 Train loss: 0.004275 Epoch 34 ------------------------------- Train loss: 0.003309 Train loss: 0.003405 Epoch 35 ------------------------------- Train loss: 0.004186 Train loss: 0.004732 Epoch 36 ------------------------------- Train loss: 0.004910 Train loss: 0.005682 Epoch 37 ------------------------------- Train loss: 0.003837 Train loss: 0.004597 Epoch 38 ------------------------------- Train loss: 0.004854 Train loss: 0.005104 Epoch 39 ------------------------------- Train loss: 0.005342 Train loss: 0.004729 Epoch 40 ------------------------------- Train loss: 0.004299 Train loss: 0.004090 Epoch 41 ------------------------------- Train loss: 0.006431 Train loss: 0.010747 Epoch 42 ------------------------------- Train loss: 0.005187 Train loss: 0.005870 Epoch 43 ------------------------------- Train loss: 0.005995 Train loss: 0.004956 Epoch 44 ------------------------------- Train loss: 0.003459 Train loss: 0.003359 Epoch 45 ------------------------------- Train loss: 0.004124 Train loss: 0.004164 Epoch 46 ------------------------------- Train loss: 0.003489 Train loss: 0.002016 Epoch 47 ------------------------------- Train loss: 0.003316 Train loss: 0.003790 Epoch 48 ------------------------------- Train loss: 0.002907 Train loss: 0.002781 Epoch 49 ------------------------------- Train loss: 0.004493 Train loss: 0.004550 Epoch 50 ------------------------------- Train loss: 0.004484 Train loss: 0.004042 Epoch 51 ------------------------------- Train loss: 0.003859 Train loss: 0.004745 Epoch 52 ------------------------------- Train loss: 0.002892 Train loss: 0.003577 Epoch 53 ------------------------------- Train loss: 0.002793 Train loss: 0.002528 Epoch 54 ------------------------------- Train loss: 0.003911 Train loss: 0.002832 Epoch 55 ------------------------------- Train loss: 0.004651 Train loss: 0.001883 Epoch 56 ------------------------------- Train loss: 0.003195 Train loss: 0.003128 Epoch 57 ------------------------------- Train loss: 0.002415 Train loss: 0.002821 Epoch 58 ------------------------------- Train loss: 0.002637 Train loss: 0.002708 Epoch 59 ------------------------------- Train loss: 0.002557 Train loss: 0.003350 Epoch 60 ------------------------------- Train loss: 0.002776 Train loss: 0.002418 Epoch 61 ------------------------------- Train loss: 0.002656 Train loss: 0.003241 Epoch 62 ------------------------------- Train loss: 0.002626 Train loss: 0.003593 Epoch 63 ------------------------------- Train loss: 0.002864 Train loss: 0.001980 Epoch 64 ------------------------------- Train loss: 0.001984 Train loss: 0.002830 Epoch 65 ------------------------------- Train loss: 0.001850 Train loss: 0.003034 Epoch 66 ------------------------------- Train loss: 0.003397 Train loss: 0.002582 Epoch 67 ------------------------------- Train loss: 0.001584 Train loss: 0.002509 Epoch 68 ------------------------------- Train loss: 0.003183 Train loss: 0.002303 Epoch 69 ------------------------------- Train loss: 0.002674 Train loss: 0.003623 Epoch 70 ------------------------------- Train loss: 0.001943 Train loss: 0.002645 Epoch 71 ------------------------------- Train loss: 0.002206 Train loss: 0.004053 Epoch 72 ------------------------------- Train loss: 0.003553 Train loss: 0.002675 Epoch 73 ------------------------------- Train loss: 0.002273 Train loss: 0.002406 Epoch 74 ------------------------------- Train loss: 0.002302 Train loss: 0.001891 Epoch 75 ------------------------------- Train loss: 0.002054 Train loss: 0.002134 Epoch 76 ------------------------------- Train loss: 0.001711 Train loss: 0.001787 Epoch 77 ------------------------------- Train loss: 0.002664 Train loss: 0.001773 Epoch 78 ------------------------------- Train loss: 0.002497 Train loss: 0.001690 Epoch 79 ------------------------------- Train loss: 0.002907 Train loss: 0.001565 Epoch 80 ------------------------------- Train loss: 0.001843 Train loss: 0.002385 Epoch 81 ------------------------------- Train loss: 0.002306 Train loss: 0.002776 Epoch 82 ------------------------------- Train loss: 0.002586 Train loss: 0.002796 Epoch 83 ------------------------------- Train loss: 0.003462 Train loss: 0.002599 Epoch 84 ------------------------------- Train loss: 0.002478 Train loss: 0.002450 Epoch 85 ------------------------------- Train loss: 0.002912 Train loss: 0.003570 Epoch 86 ------------------------------- Train loss: 0.005142 Train loss: 0.004848 Epoch 87 ------------------------------- Train loss: 0.003836 Train loss: 0.002519 Epoch 88 ------------------------------- Train loss: 0.002546 Train loss: 0.003086 Epoch 89 ------------------------------- Train loss: 0.003459 Train loss: 0.002118 Epoch 90 ------------------------------- Train loss: 0.002836 Train loss: 0.002958 Epoch 91 ------------------------------- Train loss: 0.001960 Train loss: 0.001872 Epoch 92 ------------------------------- Train loss: 0.002676 Train loss: 0.001739 Epoch 93 ------------------------------- Train loss: 0.002278 Train loss: 0.002873 Epoch 94 ------------------------------- Train loss: 0.001472 Train loss: 0.002152 Epoch 95 ------------------------------- Train loss: 0.002287 Train loss: 0.002329 Epoch 96 ------------------------------- Train loss: 0.002041 Train loss: 0.002937 Epoch 97 ------------------------------- Train loss: 0.001726 Train loss: 0.001688 Epoch 98 ------------------------------- Train loss: 0.003036 Train loss: 0.002650 Epoch 99 ------------------------------- Train loss: 0.002739 Train loss: 0.003106 Epoch 100 ------------------------------- Train loss: 0.002779 Train loss: 0.003120 Epoch 101 ------------------------------- Train loss: 0.005051 Train loss: 0.006659 Epoch 102 ------------------------------- Train loss: 0.003463 Train loss: 0.002639 Epoch 103 ------------------------------- Train loss: 0.002766 Train loss: 0.002504 Epoch 104 ------------------------------- Train loss: 0.003185 Train loss: 0.003454 Epoch 105 ------------------------------- Train loss: 0.003489 Train loss: 0.002171 Epoch 106 ------------------------------- Train loss: 0.002452 Train loss: 0.002855 Epoch 107 ------------------------------- Train loss: 0.004393 Train loss: 0.003225 Epoch 108 ------------------------------- Train loss: 0.003511 Train loss: 0.002418 Epoch 109 ------------------------------- Train loss: 0.001996 Train loss: 0.001904 Epoch 110 ------------------------------- Train loss: 0.002781 Train loss: 0.001746 Epoch 111 ------------------------------- Train loss: 0.002042 Train loss: 0.002199 Epoch 112 ------------------------------- Train loss: 0.001223 Train loss: 0.001489 Epoch 113 ------------------------------- Train loss: 0.001559 Train loss: 0.001307 Epoch 114 ------------------------------- Train loss: 0.001056 Train loss: 0.001741 Epoch 115 ------------------------------- Train loss: 0.001399 Train loss: 0.000994 Epoch 116 ------------------------------- Train loss: 0.001242 Train loss: 0.001005 Epoch 117 ------------------------------- Train loss: 0.001596 Train loss: 0.001391 Epoch 118 ------------------------------- Train loss: 0.001191 Train loss: 0.001318 Epoch 119 ------------------------------- Train loss: 0.001111 Train loss: 0.002259 Epoch 120 ------------------------------- Train loss: 0.002453 Train loss: 0.001171 Epoch 121 ------------------------------- Train loss: 0.001532 Train loss: 0.001067 Epoch 122 ------------------------------- Train loss: 0.001347 Train loss: 0.001172 Epoch 123 ------------------------------- Train loss: 0.001067 Train loss: 0.001222 Epoch 124 ------------------------------- Train loss: 0.001218 Train loss: 0.001746 Epoch 125 ------------------------------- Train loss: 0.001598 Train loss: 0.000975 Epoch 126 ------------------------------- Train loss: 0.001942 Train loss: 0.001161 Epoch 127 ------------------------------- Train loss: 0.001347 Train loss: 0.001379 Epoch 128 ------------------------------- Train loss: 0.000997 Train loss: 0.001226 Epoch 129 ------------------------------- Train loss: 0.003115 Train loss: 0.001152 Epoch 130 ------------------------------- Train loss: 0.001288 Train loss: 0.001528 Epoch 131 ------------------------------- Train loss: 0.002509 Train loss: 0.001956 Epoch 132 ------------------------------- Train loss: 0.001421 Train loss: 0.002072 Epoch 133 ------------------------------- Train loss: 0.002185 Train loss: 0.001443 Epoch 134 ------------------------------- Train loss: 0.002826 Train loss: 0.001549 Epoch 135 ------------------------------- Train loss: 0.001380 Train loss: 0.001160 Epoch 136 ------------------------------- Train loss: 0.000949 Train loss: 0.002498 Epoch 137 ------------------------------- Train loss: 0.001831 Train loss: 0.001732 Epoch 138 ------------------------------- Train loss: 0.001662 Train loss: 0.001406 Epoch 139 ------------------------------- Train loss: 0.001930 Train loss: 0.001042 Epoch 140 ------------------------------- Train loss: 0.002499 Train loss: 0.001599 Epoch 141 ------------------------------- Train loss: 0.002338 Train loss: 0.001116 Epoch 142 ------------------------------- Train loss: 0.001666 Train loss: 0.001102 Epoch 143 ------------------------------- Train loss: 0.001853 Train loss: 0.001178 Epoch 144 ------------------------------- Train loss: 0.001613 Train loss: 0.001353 Epoch 145 ------------------------------- Train loss: 0.002113 Train loss: 0.002267 Epoch 146 ------------------------------- Train loss: 0.001377 Train loss: 0.002722 Epoch 147 ------------------------------- Train loss: 0.002449 Train loss: 0.003315 Epoch 148 ------------------------------- Train loss: 0.003891 Train loss: 0.001801 Epoch 149 ------------------------------- Train loss: 0.002211 Train loss: 0.001919 Epoch 150 ------------------------------- Train loss: 0.001741 Train loss: 0.004161 Epoch 151 ------------------------------- Train loss: 0.003164 Train loss: 0.001972 Epoch 152 ------------------------------- Train loss: 0.003068 Train loss: 0.001630 Epoch 153 ------------------------------- Train loss: 0.002154 Train loss: 0.002421 Epoch 154 ------------------------------- Train loss: 0.002536 Train loss: 0.001423 Epoch 155 ------------------------------- Train loss: 0.001479 Train loss: 0.002552 Epoch 156 ------------------------------- Train loss: 0.001569 Train loss: 0.001640 Epoch 157 ------------------------------- Train loss: 0.002038 Train loss: 0.002607 Epoch 158 ------------------------------- Train loss: 0.001618 Train loss: 0.001406 Epoch 159 ------------------------------- Train loss: 0.001849 Train loss: 0.001893 Epoch 160 ------------------------------- Train loss: 0.001186 Train loss: 0.001450 Epoch 161 ------------------------------- Train loss: 0.001520 Train loss: 0.003053 Epoch 162 ------------------------------- Train loss: 0.001121 Train loss: 0.001386 Epoch 163 ------------------------------- Train loss: 0.001988 Train loss: 0.002172 Epoch 164 ------------------------------- Train loss: 0.001927 Train loss: 0.001613 Epoch 165 ------------------------------- Train loss: 0.001319 Train loss: 0.001516 Epoch 166 ------------------------------- Train loss: 0.003593 Train loss: 0.002032 Epoch 167 ------------------------------- Train loss: 0.001817 Train loss: 0.001572 Epoch 168 ------------------------------- Train loss: 0.002825 Train loss: 0.001634 Epoch 169 ------------------------------- Train loss: 0.001461 Train loss: 0.001795 Epoch 170 ------------------------------- Train loss: 0.000999 Train loss: 0.002706 Epoch 171 ------------------------------- Train loss: 0.001977 Train loss: 0.001872 Epoch 172 ------------------------------- Train loss: 0.001995 Train loss: 0.002101 Epoch 173 ------------------------------- Train loss: 0.001421 Train loss: 0.001389 Epoch 174 ------------------------------- Train loss: 0.001266 Train loss: 0.001483 Epoch 175 ------------------------------- Train loss: 0.001302 Train loss: 0.001583 Epoch 176 ------------------------------- Train loss: 0.001710 Train loss: 0.001823 Epoch 177 ------------------------------- Train loss: 0.001730 Train loss: 0.002171 Epoch 178 ------------------------------- Train loss: 0.001359 Train loss: 0.002207 Epoch 179 ------------------------------- Train loss: 0.001351 Train loss: 0.001660 Epoch 180 ------------------------------- Train loss: 0.002663 Train loss: 0.001259 Epoch 181 ------------------------------- Train loss: 0.002434 Train loss: 0.001149 Epoch 182 ------------------------------- Train loss: 0.000952 Train loss: 0.001195 Epoch 183 ------------------------------- Train loss: 0.001183 Train loss: 0.001021 Epoch 184 ------------------------------- Train loss: 0.001734 Train loss: 0.001044 Epoch 185 ------------------------------- Train loss: 0.001595 Train loss: 0.001729 Epoch 186 ------------------------------- Train loss: 0.001458 Train loss: 0.001552 Epoch 187 ------------------------------- Train loss: 0.001028 Train loss: 0.001747 Epoch 188 ------------------------------- Train loss: 0.001792 Train loss: 0.001484 Epoch 189 ------------------------------- Train loss: 0.002159 Train loss: 0.001341 Epoch 190 ------------------------------- Train loss: 0.001903 Train loss: 0.001315 Epoch 191 ------------------------------- Train loss: 0.001254 Train loss: 0.002343 Epoch 192 ------------------------------- Train loss: 0.001951 Train loss: 0.001396 Epoch 193 ------------------------------- Train loss: 0.002073 Train loss: 0.002289 Epoch 194 ------------------------------- Train loss: 0.001860 Train loss: 0.001740 Epoch 195 ------------------------------- Train loss: 0.001652 Train loss: 0.001450 Epoch 196 ------------------------------- Train loss: 0.001507 Train loss: 0.001996 Epoch 197 ------------------------------- Train loss: 0.001136 Train loss: 0.000999 Epoch 198 ------------------------------- Train loss: 0.001536 Train loss: 0.001978 Epoch 199 ------------------------------- Train loss: 0.000968 Train loss: 0.001186 Epoch 200 ------------------------------- Train loss: 0.001554 Train loss: 0.001105 Epoch 201 ------------------------------- Train loss: 0.002959 Train loss: 0.001425 Epoch 202 ------------------------------- Train loss: 0.002270 Train loss: 0.000887 Epoch 203 ------------------------------- Train loss: 0.001832 Train loss: 0.001659 Epoch 204 ------------------------------- Train loss: 0.001743 Train loss: 0.001071 Epoch 205 ------------------------------- Train loss: 0.001798 Train loss: 0.001123 Epoch 206 ------------------------------- Train loss: 0.001406 Train loss: 0.001382 Epoch 207 ------------------------------- Train loss: 0.002587 Train loss: 0.003230 Epoch 208 ------------------------------- Train loss: 0.002965 Train loss: 0.002253 Epoch 209 ------------------------------- Train loss: 0.001745 Train loss: 0.003858 Epoch 210 ------------------------------- Train loss: 0.001680 Train loss: 0.001261 Epoch 211 ------------------------------- Train loss: 0.001305 Train loss: 0.003107 Epoch 212 ------------------------------- Train loss: 0.003016 Train loss: 0.002233 Epoch 213 ------------------------------- Train loss: 0.001601 Train loss: 0.002957 Epoch 214 ------------------------------- Train loss: 0.002724 Train loss: 0.001807 Epoch 215 ------------------------------- Train loss: 0.002400 Train loss: 0.002902 Epoch 216 ------------------------------- Train loss: 0.002232 Train loss: 0.003127 Epoch 217 ------------------------------- Train loss: 0.001443 Train loss: 0.001467 Epoch 218 ------------------------------- Train loss: 0.001103 Train loss: 0.002157 Epoch 219 ------------------------------- Train loss: 0.000999 Train loss: 0.001940 Epoch 220 ------------------------------- Train loss: 0.001450 Train loss: 0.001025 Epoch 221 ------------------------------- Train loss: 0.001465 Train loss: 0.002670 Epoch 222 ------------------------------- Train loss: 0.001070 Train loss: 0.001134 Epoch 223 ------------------------------- Train loss: 0.000847 Train loss: 0.000910 Epoch 224 ------------------------------- Train loss: 0.001090 Train loss: 0.001973 Epoch 225 ------------------------------- Train loss: 0.001911 Train loss: 0.000848 Epoch 226 ------------------------------- Train loss: 0.000913 Train loss: 0.002585 Epoch 227 ------------------------------- Train loss: 0.001207 Train loss: 0.000910 Epoch 228 ------------------------------- Train loss: 0.001441 Train loss: 0.000806 Epoch 229 ------------------------------- Train loss: 0.001749 Train loss: 0.001611 Epoch 230 ------------------------------- Train loss: 0.001126 Train loss: 0.001278 Epoch 231 ------------------------------- Train loss: 0.002263 Train loss: 0.001528 Epoch 232 ------------------------------- Train loss: 0.000934 Train loss: 0.001006 Epoch 233 ------------------------------- Train loss: 0.001297 Train loss: 0.001184 Epoch 234 ------------------------------- Train loss: 0.001113 Train loss: 0.001876 Epoch 235 ------------------------------- Train loss: 0.001809 Train loss: 0.001160 Epoch 236 ------------------------------- Train loss: 0.001894 Train loss: 0.001078 Epoch 237 ------------------------------- Train loss: 0.001030 Train loss: 0.001391 Epoch 238 ------------------------------- Train loss: 0.001005 Train loss: 0.001022 Epoch 239 ------------------------------- Train loss: 0.000877 Train loss: 0.000938 Epoch 240 ------------------------------- Train loss: 0.000818 Train loss: 0.001533 Epoch 241 ------------------------------- Train loss: 0.002302 Train loss: 0.001006 Epoch 242 ------------------------------- Train loss: 0.002398 Train loss: 0.001157 Epoch 243 ------------------------------- Train loss: 0.001137 Train loss: 0.002014 Epoch 244 ------------------------------- Train loss: 0.001106 Train loss: 0.000790 Epoch 245 ------------------------------- Train loss: 0.000935 Train loss: 0.001365 Epoch 246 ------------------------------- Train loss: 0.000966 Train loss: 0.001378 Epoch 247 ------------------------------- Train loss: 0.001049 Train loss: 0.001192 Epoch 248 ------------------------------- Train loss: 0.001184 Train loss: 0.001701 Epoch 249 ------------------------------- Train loss: 0.001269 Train loss: 0.001417 Epoch 250 ------------------------------- Train loss: 0.000762 Train loss: 0.001484 Epoch 251 ------------------------------- Train loss: 0.001184 Train loss: 0.001231 Epoch 252 ------------------------------- Train loss: 0.000873 Train loss: 0.000815 Epoch 253 ------------------------------- Train loss: 0.001698 Train loss: 0.001300 Epoch 254 ------------------------------- Train loss: 0.001698 Train loss: 0.000774 Epoch 255 ------------------------------- Train loss: 0.001557 Train loss: 0.000775 Epoch 256 ------------------------------- Train loss: 0.001042 Train loss: 0.001179 Epoch 257 ------------------------------- Train loss: 0.000867 Train loss: 0.001054 Epoch 258 ------------------------------- Train loss: 0.001319 Train loss: 0.001323 Epoch 259 ------------------------------- Train loss: 0.001197 Train loss: 0.000891 Epoch 260 ------------------------------- Train loss: 0.001105 Train loss: 0.001245 Epoch 261 ------------------------------- Train loss: 0.000880 Train loss: 0.001124 Epoch 262 ------------------------------- Train loss: 0.001769 Train loss: 0.000903 Epoch 263 ------------------------------- Train loss: 0.001183 Train loss: 0.001018 Epoch 264 ------------------------------- Train loss: 0.002589 Train loss: 0.001408 Epoch 265 ------------------------------- Train loss: 0.001463 Train loss: 0.001945 Epoch 266 ------------------------------- Train loss: 0.001875 Train loss: 0.001498 Epoch 267 ------------------------------- Train loss: 0.001422 Train loss: 0.001503 Epoch 268 ------------------------------- Train loss: 0.001143 Train loss: 0.002207 Epoch 269 ------------------------------- Train loss: 0.001479 Train loss: 0.000755 Epoch 270 ------------------------------- Train loss: 0.001078 Train loss: 0.001037 Epoch 271 ------------------------------- Train loss: 0.001525 Train loss: 0.001331 Epoch 272 ------------------------------- Train loss: 0.000914 Train loss: 0.001579 Epoch 273 ------------------------------- Train loss: 0.001173 Train loss: 0.001323 Epoch 274 ------------------------------- Train loss: 0.001607 Train loss: 0.001148 Epoch 275 ------------------------------- Train loss: 0.000782 Train loss: 0.001527 Epoch 276 ------------------------------- Train loss: 0.001457 Train loss: 0.001367 Epoch 277 ------------------------------- Train loss: 0.001139 Train loss: 0.001292 Epoch 278 ------------------------------- Train loss: 0.001684 Train loss: 0.000982 Epoch 279 ------------------------------- Train loss: 0.001611 Train loss: 0.001465 Epoch 280 ------------------------------- Train loss: 0.000973 Train loss: 0.001704 Epoch 281 ------------------------------- Train loss: 0.001814 Train loss: 0.002983 Epoch 282 ------------------------------- Train loss: 0.002216 Train loss: 0.001123 Epoch 283 ------------------------------- Train loss: 0.003094 Train loss: 0.001290 Epoch 284 ------------------------------- Train loss: 0.001104 Train loss: 0.000858 Epoch 285 ------------------------------- Train loss: 0.002042 Train loss: 0.001604 Epoch 286 ------------------------------- Train loss: 0.001314 Train loss: 0.001115 Epoch 287 ------------------------------- Train loss: 0.001638 Train loss: 0.000988 Epoch 288 ------------------------------- Train loss: 0.001070 Train loss: 0.001030 Epoch 289 ------------------------------- Train loss: 0.001638 Train loss: 0.001066 Epoch 290 ------------------------------- Train loss: 0.001121 Train loss: 0.000995 Epoch 291 ------------------------------- Train loss: 0.000958 Train loss: 0.001177 Epoch 292 ------------------------------- Train loss: 0.000922 Train loss: 0.001531 Epoch 293 ------------------------------- Train loss: 0.001123 Train loss: 0.000473 Epoch 294 ------------------------------- Train loss: 0.000934 Train loss: 0.001446 Epoch 295 ------------------------------- Train loss: 0.000507 Train loss: 0.001239 Epoch 296 ------------------------------- Train loss: 0.001038 Train loss: 0.001300 Epoch 297 ------------------------------- Train loss: 0.001677 Train loss: 0.001140 Epoch 298 ------------------------------- Train loss: 0.000683 Train loss: 0.001264 Epoch 299 ------------------------------- Train loss: 0.000975 Train loss: 0.001205 Epoch 300 ------------------------------- Train loss: 0.001459 Train loss: 0.001703 Epoch 301 ------------------------------- Train loss: 0.001647 Train loss: 0.001944 Epoch 302 ------------------------------- Train loss: 0.001939 Train loss: 0.001427 Epoch 303 ------------------------------- Train loss: 0.001071 Train loss: 0.001499 Epoch 304 ------------------------------- Train loss: 0.001276 Train loss: 0.001923 Epoch 305 ------------------------------- Train loss: 0.001400 Train loss: 0.001352 Epoch 306 ------------------------------- Train loss: 0.001838 Train loss: 0.001789 Epoch 307 ------------------------------- Train loss: 0.001812 Train loss: 0.001055 Epoch 308 ------------------------------- Train loss: 0.001531 Train loss: 0.002472 Epoch 309 ------------------------------- Train loss: 0.001211 Train loss: 0.001787 Epoch 310 ------------------------------- Train loss: 0.001147 Train loss: 0.002000 Epoch 311 ------------------------------- Train loss: 0.001322 Train loss: 0.001071 Epoch 312 ------------------------------- Train loss: 0.001661 Train loss: 0.002024 Epoch 313 ------------------------------- Train loss: 0.001291 Train loss: 0.001291 Epoch 314 ------------------------------- Train loss: 0.000900 Train loss: 0.001208 Epoch 315 ------------------------------- Train loss: 0.001013 Train loss: 0.000920 Epoch 316 ------------------------------- Train loss: 0.000585 Train loss: 0.000814 Epoch 317 ------------------------------- Train loss: 0.001076 Train loss: 0.000825 Epoch 318 ------------------------------- Train loss: 0.000651 Train loss: 0.001009 Epoch 319 ------------------------------- Train loss: 0.000880 Train loss: 0.000985 Epoch 320 ------------------------------- Train loss: 0.001252 Train loss: 0.000732 Epoch 321 ------------------------------- Train loss: 0.000784 Train loss: 0.001180 Epoch 322 ------------------------------- Train loss: 0.000809 Train loss: 0.000699 Epoch 323 ------------------------------- Train loss: 0.001053 Train loss: 0.000966 Epoch 324 ------------------------------- Train loss: 0.001450 Train loss: 0.001238 Epoch 325 ------------------------------- Train loss: 0.001397 Train loss: 0.001304 Epoch 326 ------------------------------- Train loss: 0.001112 Train loss: 0.000958 Epoch 327 ------------------------------- Train loss: 0.000848 Train loss: 0.000851 Epoch 328 ------------------------------- Train loss: 0.001185 Train loss: 0.001101 Epoch 329 ------------------------------- Train loss: 0.001283 Train loss: 0.001082 Epoch 330 ------------------------------- Train loss: 0.000667 Train loss: 0.001495 Epoch 331 ------------------------------- Train loss: 0.001159 Train loss: 0.001257 Epoch 332 ------------------------------- Train loss: 0.001648 Train loss: 0.001610 Epoch 333 ------------------------------- Train loss: 0.000969 Train loss: 0.001243 Epoch 334 ------------------------------- Train loss: 0.001406 Train loss: 0.002086 Epoch 335 ------------------------------- Train loss: 0.001385 Train loss: 0.002275 Epoch 336 ------------------------------- Train loss: 0.001645 Train loss: 0.001562 Epoch 337 ------------------------------- Train loss: 0.001542 Train loss: 0.002245 Epoch 338 ------------------------------- Train loss: 0.001176 Train loss: 0.001929 Epoch 339 ------------------------------- Train loss: 0.001478 Train loss: 0.001369 Epoch 340 ------------------------------- Train loss: 0.001926 Train loss: 0.001540 Epoch 341 ------------------------------- Train loss: 0.001447 Train loss: 0.001338 Epoch 342 ------------------------------- Train loss: 0.001253 Train loss: 0.001425 Epoch 343 ------------------------------- Train loss: 0.001703 Train loss: 0.001475 Epoch 344 ------------------------------- Train loss: 0.001210 Train loss: 0.001066 Epoch 345 ------------------------------- Train loss: 0.001407 Train loss: 0.000849 Epoch 346 ------------------------------- Train loss: 0.002322 Train loss: 0.001918 Epoch 347 ------------------------------- Train loss: 0.001338 Train loss: 0.000925 Epoch 348 ------------------------------- Train loss: 0.000811 Train loss: 0.000882 Epoch 349 ------------------------------- Train loss: 0.001076 Train loss: 0.001281 Epoch 350 ------------------------------- Train loss: 0.001611 Train loss: 0.001537 Epoch 351 ------------------------------- Train loss: 0.000888 Train loss: 0.001229 Epoch 352 ------------------------------- Train loss: 0.001257 Train loss: 0.001495 Epoch 353 ------------------------------- Train loss: 0.000672 Train loss: 0.001205 Epoch 354 ------------------------------- Train loss: 0.001665 Train loss: 0.000858 Epoch 355 ------------------------------- Train loss: 0.000742 Train loss: 0.001388 Epoch 356 ------------------------------- Train loss: 0.000813 Train loss: 0.000931 Epoch 357 ------------------------------- Train loss: 0.000947 Train loss: 0.000923 Epoch 358 ------------------------------- Train loss: 0.001463 Train loss: 0.000556 Epoch 359 ------------------------------- Train loss: 0.000956 Train loss: 0.001306 Epoch 360 ------------------------------- Train loss: 0.000941 Train loss: 0.000815 Epoch 361 ------------------------------- Train loss: 0.001175 Train loss: 0.000952 Epoch 362 ------------------------------- Train loss: 0.000800 Train loss: 0.000844 Epoch 363 ------------------------------- Train loss: 0.000797 Train loss: 0.000928 Epoch 364 ------------------------------- Train loss: 0.000607 Train loss: 0.000802 Epoch 365 ------------------------------- Train loss: 0.000915 Train loss: 0.001059 Epoch 366 ------------------------------- Train loss: 0.000671 Train loss: 0.000766 Epoch 367 ------------------------------- Train loss: 0.000681 Train loss: 0.000728 Epoch 368 ------------------------------- Train loss: 0.001085 Train loss: 0.000590 Epoch 369 ------------------------------- Train loss: 0.001679 Train loss: 0.001105 Epoch 370 ------------------------------- Train loss: 0.000851 Train loss: 0.001118 Epoch 371 ------------------------------- Train loss: 0.000972 Train loss: 0.001354 Epoch 372 ------------------------------- Train loss: 0.000809 Train loss: 0.000854 Epoch 373 ------------------------------- Train loss: 0.001202 Train loss: 0.001271 Epoch 374 ------------------------------- Train loss: 0.001206 Train loss: 0.000769 Epoch 375 ------------------------------- Train loss: 0.001688 Train loss: 0.001228 Epoch 376 ------------------------------- Train loss: 0.000609 Train loss: 0.000879 Epoch 377 ------------------------------- Train loss: 0.001056 Train loss: 0.001008 Epoch 378 ------------------------------- Train loss: 0.000902 Train loss: 0.001265 Epoch 379 ------------------------------- Train loss: 0.001559 Train loss: 0.001559 Epoch 380 ------------------------------- Train loss: 0.001156 Train loss: 0.000880 Epoch 381 ------------------------------- Train loss: 0.002341 Train loss: 0.001118 Epoch 382 ------------------------------- Train loss: 0.002283 Train loss: 0.001703 Epoch 383 ------------------------------- Train loss: 0.001116 Train loss: 0.001527 Epoch 384 ------------------------------- Train loss: 0.001842 Train loss: 0.001128 Epoch 385 ------------------------------- Train loss: 0.001422 Train loss: 0.001150 Epoch 386 ------------------------------- Train loss: 0.002275 Train loss: 0.001356 Epoch 387 ------------------------------- Train loss: 0.000671 Train loss: 0.001114 Epoch 388 ------------------------------- Train loss: 0.000937 Train loss: 0.001217 Epoch 389 ------------------------------- Train loss: 0.001098 Train loss: 0.001419 Epoch 390 ------------------------------- Train loss: 0.001127 Train loss: 0.001972 Epoch 391 ------------------------------- Train loss: 0.001067 Train loss: 0.000787 Epoch 392 ------------------------------- Train loss: 0.001496 Train loss: 0.000962 Epoch 393 ------------------------------- Train loss: 0.001695 Train loss: 0.001483 Epoch 394 ------------------------------- Train loss: 0.001596 Train loss: 0.001696 Epoch 395 ------------------------------- Train loss: 0.001463 Train loss: 0.000781 Epoch 396 ------------------------------- Train loss: 0.000982 Train loss: 0.001366 Epoch 397 ------------------------------- Train loss: 0.001226 Train loss: 0.001634 Epoch 398 ------------------------------- Train loss: 0.001184 Train loss: 0.002013 Epoch 399 ------------------------------- Train loss: 0.001252 Train loss: 0.001260 Epoch 400 ------------------------------- Train loss: 0.000760 Train loss: 0.001206 Epoch 401 ------------------------------- Train loss: 0.000525 Train loss: 0.000622 Epoch 402 ------------------------------- Train loss: 0.000923 Train loss: 0.000783 Epoch 403 ------------------------------- Train loss: 0.000561 Train loss: 0.001412 Epoch 404 ------------------------------- Train loss: 0.000731 Train loss: 0.001244 Epoch 405 ------------------------------- Train loss: 0.001769 Train loss: 0.001109 Epoch 406 ------------------------------- Train loss: 0.001085 Train loss: 0.001238 Epoch 407 ------------------------------- Train loss: 0.000673 Train loss: 0.000890 Epoch 408 ------------------------------- Train loss: 0.000873 Train loss: 0.000812 Epoch 409 ------------------------------- Train loss: 0.000592 Train loss: 0.000773 Epoch 410 ------------------------------- Train loss: 0.000774 Train loss: 0.000976 Epoch 411 ------------------------------- Train loss: 0.001167 Train loss: 0.000695 Epoch 412 ------------------------------- Train loss: 0.000525 Train loss: 0.000793 Epoch 413 ------------------------------- Train loss: 0.001078 Train loss: 0.000808 Epoch 414 ------------------------------- Train loss: 0.000685 Train loss: 0.000771 Epoch 415 ------------------------------- Train loss: 0.000423 Train loss: 0.000861 Epoch 416 ------------------------------- Train loss: 0.001048 Train loss: 0.000867 Epoch 417 ------------------------------- Train loss: 0.000647 Train loss: 0.001454 Epoch 418 ------------------------------- Train loss: 0.000711 Train loss: 0.000876 Epoch 419 ------------------------------- Train loss: 0.000572 Train loss: 0.000988 Epoch 420 ------------------------------- Train loss: 0.001614 Train loss: 0.000844 Epoch 421 ------------------------------- Train loss: 0.001105 Train loss: 0.000728 Epoch 422 ------------------------------- Train loss: 0.001853 Train loss: 0.002423 Epoch 423 ------------------------------- Train loss: 0.001806 Train loss: 0.002750 Epoch 424 ------------------------------- Train loss: 0.000941 Train loss: 0.001574 Epoch 425 ------------------------------- Train loss: 0.001870 Train loss: 0.000616 Epoch 426 ------------------------------- Train loss: 0.001422 Train loss: 0.001109 Epoch 427 ------------------------------- Train loss: 0.001623 Train loss: 0.001718 Epoch 428 ------------------------------- Train loss: 0.001297 Train loss: 0.001605 Epoch 429 ------------------------------- Train loss: 0.001071 Train loss: 0.002339 Epoch 430 ------------------------------- Train loss: 0.001330 Train loss: 0.002031 Epoch 431 ------------------------------- Train loss: 0.000830 Train loss: 0.001056 Epoch 432 ------------------------------- Train loss: 0.001187 Train loss: 0.001332 Epoch 433 ------------------------------- Train loss: 0.000953 Train loss: 0.000903 Epoch 434 ------------------------------- Train loss: 0.000662 Train loss: 0.001240 Epoch 435 ------------------------------- Train loss: 0.001170 Train loss: 0.000849 Epoch 436 ------------------------------- Train loss: 0.000755 Train loss: 0.000869 Epoch 437 ------------------------------- Train loss: 0.000713 Train loss: 0.000681 Epoch 438 ------------------------------- Train loss: 0.000949 Train loss: 0.000767 Epoch 439 ------------------------------- Train loss: 0.000583 Train loss: 0.000713 Epoch 440 ------------------------------- Train loss: 0.000521 Train loss: 0.000866 Epoch 441 ------------------------------- Train loss: 0.000411 Train loss: 0.000641 Epoch 442 ------------------------------- Train loss: 0.000923 Train loss: 0.000773 Epoch 443 ------------------------------- Train loss: 0.000608 Train loss: 0.000481 Epoch 444 ------------------------------- Train loss: 0.000664 Train loss: 0.000551 Epoch 445 ------------------------------- Train loss: 0.000721 Train loss: 0.000745 Epoch 446 ------------------------------- Train loss: 0.000736 Train loss: 0.000930 Epoch 447 ------------------------------- Train loss: 0.000512 Train loss: 0.000540 Epoch 448 ------------------------------- Train loss: 0.000524 Train loss: 0.000533 Epoch 449 ------------------------------- Train loss: 0.000402 Train loss: 0.000997 Epoch 450 ------------------------------- Train loss: 0.000849 Train loss: 0.000858 Epoch 451 ------------------------------- Train loss: 0.000651 Train loss: 0.000491 Epoch 452 ------------------------------- Train loss: 0.000523 Train loss: 0.000569 Epoch 453 ------------------------------- Train loss: 0.000660 Train loss: 0.001007 Epoch 454 ------------------------------- Train loss: 0.000780 Train loss: 0.000704 Epoch 455 ------------------------------- Train loss: 0.000746 Train loss: 0.000898 Epoch 456 ------------------------------- Train loss: 0.000638 Train loss: 0.000573 Epoch 457 ------------------------------- Train loss: 0.000826 Train loss: 0.000681 Epoch 458 ------------------------------- Train loss: 0.001039 Train loss: 0.000903 Epoch 459 ------------------------------- Train loss: 0.000956 Train loss: 0.001038 Epoch 460 ------------------------------- Train loss: 0.000571 Train loss: 0.000862 Epoch 461 ------------------------------- Train loss: 0.000965 Train loss: 0.000954 Epoch 462 ------------------------------- Train loss: 0.001670 Train loss: 0.001610 Epoch 463 ------------------------------- Train loss: 0.001011 Train loss: 0.001551 Epoch 464 ------------------------------- Train loss: 0.000996 Train loss: 0.000699 Epoch 465 ------------------------------- Train loss: 0.001285 Train loss: 0.001792 Epoch 466 ------------------------------- Train loss: 0.001534 Train loss: 0.000984 Epoch 467 ------------------------------- Train loss: 0.001357 Train loss: 0.000974 Epoch 468 ------------------------------- Train loss: 0.001358 Train loss: 0.002237 Epoch 469 ------------------------------- Train loss: 0.001865 Train loss: 0.001172 Epoch 470 ------------------------------- Train loss: 0.001156 Train loss: 0.001132 Epoch 471 ------------------------------- Train loss: 0.000937 Train loss: 0.001708 Epoch 472 ------------------------------- Train loss: 0.001377 Train loss: 0.001699 Epoch 473 ------------------------------- Train loss: 0.001161 Train loss: 0.001148 Epoch 474 ------------------------------- Train loss: 0.000758 Train loss: 0.000880 Epoch 475 ------------------------------- Train loss: 0.000791 Train loss: 0.000613 Epoch 476 ------------------------------- Train loss: 0.000600 Train loss: 0.000758 Epoch 477 ------------------------------- Train loss: 0.000681 Train loss: 0.001436 Epoch 478 ------------------------------- Train loss: 0.000688 Train loss: 0.001033 Epoch 479 ------------------------------- Train loss: 0.000825 Train loss: 0.001004 Epoch 480 ------------------------------- Train loss: 0.000717 Train loss: 0.000790 Epoch 481 ------------------------------- Train loss: 0.002253 Train loss: 0.001060 Epoch 482 ------------------------------- Train loss: 0.001348 Train loss: 0.001656 Epoch 483 ------------------------------- Train loss: 0.001193 Train loss: 0.001694 Epoch 484 ------------------------------- Train loss: 0.000719 Train loss: 0.000674 Epoch 485 ------------------------------- Train loss: 0.001485 Train loss: 0.001226 Epoch 486 ------------------------------- Train loss: 0.001289 Train loss: 0.001610 Epoch 487 ------------------------------- Train loss: 0.000985 Train loss: 0.001612 Epoch 488 ------------------------------- Train loss: 0.001365 Train loss: 0.001342 Epoch 489 ------------------------------- Train loss: 0.001654 Train loss: 0.000671 Epoch 490 ------------------------------- Train loss: 0.001050 Train loss: 0.000807 Epoch 491 ------------------------------- Train loss: 0.000878 Train loss: 0.001021 Epoch 492 ------------------------------- Train loss: 0.000543 Train loss: 0.000829 Epoch 493 ------------------------------- Train loss: 0.000471 Train loss: 0.000649 Epoch 494 ------------------------------- Train loss: 0.000683 Train loss: 0.000949 Epoch 495 ------------------------------- Train loss: 0.000996 Train loss: 0.000606 Epoch 496 ------------------------------- Train loss: 0.000906 Train loss: 0.000579 Epoch 497 ------------------------------- Train loss: 0.000565 Train loss: 0.000328 Epoch 498 ------------------------------- Train loss: 0.000557 Train loss: 0.000732 Epoch 499 ------------------------------- Train loss: 0.000399 Train loss: 0.000831 Epoch 500 ------------------------------- Train loss: 0.000394 Train loss: 0.000582 Done!
import soundfile as sf
cnn_2d.to('cpu')
with torch.no_grad():
Xt, sr=librosa.load('//content/drive/MyDrive/deep learning/Assignment2/train_dirty_male.wav', sr= None)
Xt=librosa.stft(Xt, n_fft=1024, hop_length=512)
Xt_abs = np.abs(Xt)
Xt_abs.astype(np.float32)
Xt_abs_T = Xt_abs.T
converted_Xt = np.array(convert_voice_to_img(Xt_abs_T))
y = np.asarray(cnn_2d(torch.tensor(converted_Xt)))
q = [[0] * 513] * 19
q = np.array(q)
y = np.concatenate((q, y), axis=0)
y = y.transpose()
y = np.abs(y) * ((Xt/ Xt_abs))
y = librosa.istft(y, hop_length=512)
sf.write('Qs3_train_clean_male_recons_cnn2D.wav', y, sr)
s, sr = librosa.load('/content/drive/MyDrive/deep learning/Assignment2/train_clean_male.wav', sr= None)
s_hat, sr_hat = librosa.load('/content/Qs3_train_clean_male_recons_cnn2D.wav', sr= None)
I tried many different layers, channels, kernel sizes, different hidden layers, paddings, and different number of nodes, but this is the best result I got.
print(snr(s[:len(s_hat)], s_hat))
-1.7367381034945597
import soundfile as sf
cnn_2d.to('cpu')
with torch.no_grad():
Xt, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/test_x_01.wav', sr= None)
Xt=librosa.stft(Xt, n_fft=1024, hop_length=512)
Xt_abs = np.abs(Xt)
Xt_abs.astype(np.float32)
Xt_abs_T = Xt_abs.T
converted_Xt = np.array(convert_voice_to_img(Xt_abs_T))
y = np.asarray(cnn_2d(torch.tensor(converted_Xt)))
q = [[0] * 513] * 19
q = np.array(q)
y = np.concatenate((q, y), axis=0)
y = y.transpose()
y = np.abs(y) * ((Xt/ Xt_abs))
y = librosa.istft(y, hop_length=512)
sf.write('Qs3_test_s_01_recons_cnn2D.wav', y, sr)
import soundfile as sf
cnn_2d.to('cpu')
with torch.no_grad():
Xt, sr=librosa.load('/content/drive/MyDrive/deep learning/Assignment2/test_x_02.wav', sr= None)
Xt=librosa.stft(Xt, n_fft=1024, hop_length=512)
Xt_abs = np.abs(Xt)
Xt_abs.astype(np.float32)
Xt_abs_T = Xt_abs.T
converted_Xt = np.array(convert_voice_to_img(Xt_abs_T))
y = np.asarray(cnn_2d(torch.tensor(converted_Xt)))
q = [[0] * 513] * 19
q = np.array(q)
y = np.concatenate((q, y), axis=0)
y = y.transpose()
y = np.abs(y) * ((Xt/ Xt_abs))
y = librosa.istft(y, hop_length=512)
sf.write('Qs3_test_s_02_recons_cnn2D.wav', y, sr)
!pip install nbconvert
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (5.6.1) Requirement already satisfied: nbformat>=4.4 in /usr/local/lib/python3.7/dist-packages (from nbconvert) (5.7.0) Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert) (0.8.4) Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert) (0.4) Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert) (1.5.0) Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert) (0.7.1) Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/dist-packages (from nbconvert) (4.11.1) Requirement already satisfied: jinja2>=2.4 in /usr/local/lib/python3.7/dist-packages (from nbconvert) (2.11.3) Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from nbconvert) (2.6.1) Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert) (5.0.1) Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert) (0.6.0) Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert) (5.1.1) Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2>=2.4->nbconvert) (2.0.1) Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.4->nbconvert) (4.3.3) Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.4->nbconvert) (2.16.2) Requirement already satisfied: importlib-metadata>=3.6 in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.4->nbconvert) (5.0.0) Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=3.6->nbformat>=4.4->nbconvert) (4.1.1) Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=3.6->nbformat>=4.4->nbconvert) (3.9.0) Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat>=4.4->nbconvert) (5.10.0) Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat>=4.4->nbconvert) (22.1.0) Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat>=4.4->nbconvert) (0.18.1) Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert) (1.15.0) Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert) (0.5.1)
!jupyter nbconvert --to html dl_a2.ipynb
[NbConvertApp] Converting notebook dl_a2.ipynb to html [NbConvertApp] Writing 525877 bytes to dl_a2.html
import IPython
# Question 1, output of the model trained on the whole dataset without validationset
IPython.display.Audio("/content/Qs1_train_clean_male_recons_v1.wav")
# Question 1, output of the model trained on only the training set
IPython.display.Audio("/content/Qs1_train_clean_male_recons_v2.wav")
# Question 1, Test1
IPython.display.Audio("/content/Qs1_test_s_01_recons.wav")
# Question 1, Test2
IPython.display.Audio("/content/Qs1_test_s_02_recons.wav")
# Question 2
IPython.display.Audio("/content/Qs2_train_clean_male_recons_cnn1D.wav")
# Question 2, Test1
IPython.display.Audio("/content/Qs2_test_s_01_recons_cnn1D.wav")
# Question 2, Test2
IPython.display.Audio("/content/Qs2_test_s_02_recons_cnn1D.wav")
# Question 3
IPython.display.Audio("/content/Qs3_train_clean_male_recons_cnn2D.wav")
# Question 3, Test1
IPython.display.Audio("//content/Qs3_test_s_01_recons_cnn2D.wav")
# Question 3, Test2
IPython.display.Audio("/content/Qs3_test_s_02_recons_cnn2D.wav")